Data Mining is concerned with the discovery of interesting patterns and knowledge in data
repositories. Cluster Analysis which belongs to the core methods of data mining is the process
of discovering homogeneous groups called clusters. Given a data-set and some measure of
similarity between data objects, the goal in most clustering algorithms is maximizing both the
homogeneity within each cluster and the heterogeneity between different clusters. In this work,
two multilevel algorithms for the clustering problem are introduced. The multilevel
paradigm suggests looking at the clustering problem as a hierarchical optimization process
going through different levels evolving from a coarse grain to fine grain strategy. The clustering
problem is solved by first reducing the problem level by level to a coarser problem where an
initial clustering is computed. The clustering of the coarser problem is mapped back level-bylevel
to obtain a better clustering of the original problem by refining the intermediate different
clustering obtained at various levels. A benchmark using a number of data sets collected from a
variety of domains is used to compare the effectiveness of the hierarchical approach against its
single-level counterpart.
This document provides an overview of several clustering algorithms. It begins by defining clustering and its importance in data mining. It then categorizes clustering algorithms into four main types: partitional, hierarchical, grid-based, and density-based. For each type, some representative algorithms are described briefly. The document also reviews several popular clustering algorithms like k-means, CLARA, PAM, CLARANS, and BIRCH in more detail. It discusses aspects like the algorithms' time complexity, types of data handled, ability to detect clusters of different shapes, required input parameters, and advantages/disadvantages. Overall, the document aims to guide selection of suitable clustering algorithms for specific applications by surveying their key characteristics.
Evaluating the Use of Clustering for Automatically Organising Digital Library...pathsproject
Paper by Mark M. Hall, Mark Stevenson and Paul D. Clough from the Information School /Department of Computer Science, University of Sheffield, UK
24-27 September 2012
TPDL 2012, Cyprus
Novel text categorization by amalgamation of augmented k nearest neighbourhoo...ijcsity
Machine learning for text classification is the
underpinning
of document
cataloging
, news filtering,
document
steering
and
exemplif
ication
. In text mining realm, effective feature selection is significant to
make the learning task more accurate and competent. One of the
traditional
lazy
text classifier
k
-
Nearest
Neighborhood (
k
NN) has
a
major pitfall in calculating the similarity between
all
the
objects in training and
testing se
t
s,
there by leads to exaggeration of
both
computational complexity
of the algorithm
and
massive
consumption
of
main memory
. To diminish these shortcomings
in
viewpoint
of a
data
-
mining
practitioner
a
n
amalgamati
ve technique is proposed in this paper using
a novel restructured version of
k
NN called
Augmented
k
NN
(AkNN)
and
k
-
Medoids
(kMdd)
clustering.
The proposed work
comprises
preprocesses
on
the
initial training
set
by
imposing
attribute feature selection
for reduc
tion of high dimensionality, also it
detects and excludes the high
-
fliers
samples
in t
he
initial
training set
and
re
structure
s
a
constricted
training
set
.
The kMdd clustering algorithm generates the cluster centers (as interior objects) for each category
and
restructures
the constricted training set
with centroids
. This technique
is
amalgamated with
AkNN
classifier
that
was prearranged with
text mining similarity measure
s.
Eventually, s
ignifican
tweights
and ranks were
assigned to each object in the new
training set based upon the
ir
accessory towards the
object in testing set
.
Experiments
conducted
on Reuters
-
21578 a
UCI benchmark
text mining
data
set
, and
comparisons with
traditional
k
NN
classifier designates
the
referred
method
yield
spreeminentrecital
in b
oth clustering and
classification
This document summarizes a research paper that evaluates cluster quality using a modified density subspace clustering approach. It discusses how density subspace clustering can be used to identify clusters in high-dimensional datasets by detecting density-connected clusters in all subspaces. The proposed approach uses a density subspace clustering algorithm to select attribute subsets and identify the best clusters. It then calculates intra-cluster and inter-cluster distances to evaluate cluster quality and compares the results to other clustering algorithms in terms of accuracy and runtime. Experimental results showed that the proposed method improves clustering quality and performs faster than existing techniques.
This document discusses various machine learning classifiers that have been used for emotion recognition from speech, including neural networks, Gaussian mixture models, linear regression, and decision trees. Neural networks are identified as the most suitable classifier for this complex problem due to their ability to learn patterns from data and model complex nonlinear relationships. The document provides details on different neural network architectures and training methods that have been employed for emotion recognition from speech in previous studies.
PROBABILITY BASED CLUSTER EXPANSION OVERSAMPLING TECHNIQUE FOR IMBALANCED DATAcscpconf
In many applications of data mining, class imbalance is noticed when examples in one class are
overrepresented. Traditional classifiers result in poor accuracy of the minority class due to the
class imbalance. Further, the presence of within class imbalance where classes are composed of
multiple sub-concepts with different number of examples also affect the performance of
classifier. In this paper, we propose an oversampling technique that handles between class and
within class imbalance simultaneously and also takes into consideration the generalization
ability in data space. The proposed method is based on two steps- performing Model Based
Clustering with respect to classes to identify the sub-concepts; and then computing the
separating hyperplane based on equal posterior probability between the classes. The proposed
method is tested on 10 publicly available data sets and the result shows that the proposed
method is statistically superior to other existing oversampling methods.
Extensive Analysis on Generation and Consensus Mechanisms of Clustering Ensem...IJECEIAES
Data analysis plays a prominent role in interpreting various phenomena. Data mining is the process to hypothesize useful knowledge from the extensive data. Based upon the classical statistical prototypes the data can be exploited beyond the storage and management of the data. Cluster analysis a primary investigation with little or no prior knowledge, consists of research and development across a wide variety of communities. Cluster ensembles are melange of individual solutions obtained from different clusterings to produce final quality clustering which is required in wider applications. The method arises in the perspective of increasing robustness, scalability and accuracy. This paper gives a brief overview of the generation methods and consensus functions included in cluster ensemble. The survey is to analyze the various techniques and cluster ensemble methods.
Cluster analysis is an unsupervised learning technique used to group unlabeled data points into meaningful clusters. There are several approaches to cluster analysis including partitioning methods like k-means, hierarchical clustering methods like agglomerative nesting (AGNES), and density-based methods like DBSCAN. The quality of clusters is evaluated based on intra-cluster similarity and inter-cluster dissimilarity. Cluster analysis has applications in fields like pattern recognition, image processing, and market segmentation.
This document provides an overview of several clustering algorithms. It begins by defining clustering and its importance in data mining. It then categorizes clustering algorithms into four main types: partitional, hierarchical, grid-based, and density-based. For each type, some representative algorithms are described briefly. The document also reviews several popular clustering algorithms like k-means, CLARA, PAM, CLARANS, and BIRCH in more detail. It discusses aspects like the algorithms' time complexity, types of data handled, ability to detect clusters of different shapes, required input parameters, and advantages/disadvantages. Overall, the document aims to guide selection of suitable clustering algorithms for specific applications by surveying their key characteristics.
Evaluating the Use of Clustering for Automatically Organising Digital Library...pathsproject
Paper by Mark M. Hall, Mark Stevenson and Paul D. Clough from the Information School /Department of Computer Science, University of Sheffield, UK
24-27 September 2012
TPDL 2012, Cyprus
Novel text categorization by amalgamation of augmented k nearest neighbourhoo...ijcsity
Machine learning for text classification is the
underpinning
of document
cataloging
, news filtering,
document
steering
and
exemplif
ication
. In text mining realm, effective feature selection is significant to
make the learning task more accurate and competent. One of the
traditional
lazy
text classifier
k
-
Nearest
Neighborhood (
k
NN) has
a
major pitfall in calculating the similarity between
all
the
objects in training and
testing se
t
s,
there by leads to exaggeration of
both
computational complexity
of the algorithm
and
massive
consumption
of
main memory
. To diminish these shortcomings
in
viewpoint
of a
data
-
mining
practitioner
a
n
amalgamati
ve technique is proposed in this paper using
a novel restructured version of
k
NN called
Augmented
k
NN
(AkNN)
and
k
-
Medoids
(kMdd)
clustering.
The proposed work
comprises
preprocesses
on
the
initial training
set
by
imposing
attribute feature selection
for reduc
tion of high dimensionality, also it
detects and excludes the high
-
fliers
samples
in t
he
initial
training set
and
re
structure
s
a
constricted
training
set
.
The kMdd clustering algorithm generates the cluster centers (as interior objects) for each category
and
restructures
the constricted training set
with centroids
. This technique
is
amalgamated with
AkNN
classifier
that
was prearranged with
text mining similarity measure
s.
Eventually, s
ignifican
tweights
and ranks were
assigned to each object in the new
training set based upon the
ir
accessory towards the
object in testing set
.
Experiments
conducted
on Reuters
-
21578 a
UCI benchmark
text mining
data
set
, and
comparisons with
traditional
k
NN
classifier designates
the
referred
method
yield
spreeminentrecital
in b
oth clustering and
classification
This document summarizes a research paper that evaluates cluster quality using a modified density subspace clustering approach. It discusses how density subspace clustering can be used to identify clusters in high-dimensional datasets by detecting density-connected clusters in all subspaces. The proposed approach uses a density subspace clustering algorithm to select attribute subsets and identify the best clusters. It then calculates intra-cluster and inter-cluster distances to evaluate cluster quality and compares the results to other clustering algorithms in terms of accuracy and runtime. Experimental results showed that the proposed method improves clustering quality and performs faster than existing techniques.
This document discusses various machine learning classifiers that have been used for emotion recognition from speech, including neural networks, Gaussian mixture models, linear regression, and decision trees. Neural networks are identified as the most suitable classifier for this complex problem due to their ability to learn patterns from data and model complex nonlinear relationships. The document provides details on different neural network architectures and training methods that have been employed for emotion recognition from speech in previous studies.
PROBABILITY BASED CLUSTER EXPANSION OVERSAMPLING TECHNIQUE FOR IMBALANCED DATAcscpconf
In many applications of data mining, class imbalance is noticed when examples in one class are
overrepresented. Traditional classifiers result in poor accuracy of the minority class due to the
class imbalance. Further, the presence of within class imbalance where classes are composed of
multiple sub-concepts with different number of examples also affect the performance of
classifier. In this paper, we propose an oversampling technique that handles between class and
within class imbalance simultaneously and also takes into consideration the generalization
ability in data space. The proposed method is based on two steps- performing Model Based
Clustering with respect to classes to identify the sub-concepts; and then computing the
separating hyperplane based on equal posterior probability between the classes. The proposed
method is tested on 10 publicly available data sets and the result shows that the proposed
method is statistically superior to other existing oversampling methods.
Extensive Analysis on Generation and Consensus Mechanisms of Clustering Ensem...IJECEIAES
Data analysis plays a prominent role in interpreting various phenomena. Data mining is the process to hypothesize useful knowledge from the extensive data. Based upon the classical statistical prototypes the data can be exploited beyond the storage and management of the data. Cluster analysis a primary investigation with little or no prior knowledge, consists of research and development across a wide variety of communities. Cluster ensembles are melange of individual solutions obtained from different clusterings to produce final quality clustering which is required in wider applications. The method arises in the perspective of increasing robustness, scalability and accuracy. This paper gives a brief overview of the generation methods and consensus functions included in cluster ensemble. The survey is to analyze the various techniques and cluster ensemble methods.
Cluster analysis is an unsupervised learning technique used to group unlabeled data points into meaningful clusters. There are several approaches to cluster analysis including partitioning methods like k-means, hierarchical clustering methods like agglomerative nesting (AGNES), and density-based methods like DBSCAN. The quality of clusters is evaluated based on intra-cluster similarity and inter-cluster dissimilarity. Cluster analysis has applications in fields like pattern recognition, image processing, and market segmentation.
Cancer data partitioning with data structure and difficulty independent clust...IRJET Journal
This document discusses cancer data partitioning using clustering techniques. It begins with an introduction to clustering concepts and different clustering methods like k-means, hierarchical agglomerative clustering, and partitioning methods. It then reviews literature on clustering algorithms and ensemble methods applied to problems like speaker diarization and tumor clustering from gene expression data. The document analyzes issues with existing clustering methodology and proposes a new dynamic ensemble membership selection scheme to support data structure and complexity independent clustering for cancer data partitioning. The method combines partition around medoids clustering with an incremental semi-supervised cluster ensemble framework to improve healthcare data partitioning accuracy.
Building a Classifier Employing Prism Algorithm with Fuzzy LogicIJDKP
Classification in data mining is receiving immense interest in recent times. As the knowledge is based on
historical data, classifications of data are essential for discovering the knowledge. To decrease the
classification complexity, the quantitative attributes of data need splitting. But the splitting using the
classical logic is less accurate. This can be overcome by the use of fuzzy logic. This paper illustrates how to
build up the classification rules using the fuzzy logic. The fuzzy classifier is built on by using the prism
decision tree algorithm. This classifier produces more realistic results than the classical one. The
effectiveness of this method is justified over a sample dataset.
This document summarizes Chapter 10 of the book "Data Mining: Concepts and Techniques (3rd ed.)" which covers cluster analysis. The chapter introduces different types of clustering methods including partitioning methods like k-means and k-medoids, hierarchical methods, density-based methods, and grid-based methods. It discusses how to evaluate the quality of clustering results and highlights considerations for cluster analysis such as similarity measures, clustering space, and challenges like scalability and high dimensionality.
Cluster analysis is an unsupervised machine learning technique that groups similar data objects into clusters. It finds internal structures within unlabeled data by partitioning it into groups based on similarity. Some key applications of cluster analysis include market segmentation, document classification, and identifying subtypes of diseases. The quality of clusters depends on both the similarity measure used and how well objects are grouped within each cluster versus across clusters.
Textual Data Partitioning with Relationship and Discriminative AnalysisEditor IJMTER
Data partitioning methods are used to partition the data values with similarity. Similarity
measures are used to estimate transaction relationships. Hierarchical clustering model produces tree
structured results. Partitioned clustering produces results in grid format. Text documents are
unstructured data values with high dimensional attributes. Document clustering group ups unlabeled text
documents into meaningful clusters. Traditional clustering methods require cluster count (K) for the
document grouping process. Clustering accuracy degrades drastically with reference to the unsuitable
cluster count.
Textual data elements are divided into two types’ discriminative words and nondiscriminative
words. Only discriminative words are useful for grouping documents. The involvement of
nondiscriminative words confuses the clustering process and leads to poor clustering solution in return.
A variation inference algorithm is used to infer the document collection structure and partition of
document words at the same time. Dirichlet Process Mixture (DPM) model is used to partition
documents. DPM clustering model uses both the data likelihood and the clustering property of the
Dirichlet Process (DP). Dirichlet Process Mixture Model for Feature Partition (DPMFP) is used to
discover the latent cluster structure based on the DPM model. DPMFP clustering is performed without
requiring the number of clusters as input.
Document labels are used to estimate the discriminative word identification process. Concept
relationships are analyzed with Ontology support. Semantic weight model is used for the document
similarity analysis. The system improves the scalability with the support of labels and concept relations
for dimensionality reduction process.
A Novel Approach to Mathematical Concepts in Data Miningijdmtaiir
-This paper describes three different fundamental
mathematical programming approaches that are relevant to
data mining. They are: Feature Selection, Clustering and
Robust Representation. This paper comprises of two clustering
algorithms such as K-mean algorithm and K-median
algorithms. Clustering is illustrated by the unsupervised
learning of patterns and clusters that may exist in a given
databases and useful tool for Knowledge Discovery in
Database (KDD). The results of k-median algorithm are used
to collecting the blood cancer patient from a medical database.
K-mean clustering is a data mining/machine learning algorithm
used to cluster observations into groups of related observations
without any prior knowledge of those relationships. The kmean algorithm is one of the simplest clustering techniques
and it is commonly used in medical imaging, biometrics and
related fields.
The document discusses different clustering techniques used for grouping large amounts of data. It covers partitioning methods like k-means and k-medoids that organize data into exclusive groups. It also describes hierarchical methods like agglomerative and divisive clustering that arrange data into nested groups or trees. Additionally, it mentions density-based and grid-based clustering and provides algorithms for different clustering approaches.
Step by step operations by which we make a group of objects in which attributes
of all the objects are nearly similar, known as clustering. So, a cluster is a collection of
objects that acquire nearly same attribute values. The property of an object in a cluster is
similar to other objects in same cluster but different with objects of other clusters.
Clustering is used in wide range of applications like pattern recognition, image processing,
data analysis, machine learning etc. Nowadays, more attention has been put on categorical
data rather than numerical data. Where, the range of numerical attributes organizes in a
class like small, medium, high, and so on. There is wide range of algorithm that used to
make clusters of given categorical data. Our approach is to enhance the working on well-
known clustering algorithm k-modes to improve accuracy of algorithm. We proposed a new
approach named “High Accuracy Clustering Algorithm for Categorical datasets”.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
This document discusses using genetic programming (GP) for data classification. It begins with an introduction to classification problems and algorithms like decision trees, neural networks, and Bayesian classification. It then provides background on genetic algorithms and genetic programming, explaining that GP uses tree-based representations of computer programs to evolve solutions. The document presents a case study on using GP for classification, discussing fitness measures, creating training sets, and an interleaved data format to address class imbalance. The goal is to automatically generate classification models for data sets using GP.
Enhanced Clustering Algorithm for Processing Online DataIOSR Journals
This document proposes an enhanced incremental clustering algorithm for processing online data. It discusses existing clustering algorithms like leader clustering and hierarchical clustering, which have limitations in handling dynamic data. The proposed algorithm aims to dynamically create initial clusters and rearrange clusters based on data characteristics, allowing for more accurate clustering of online data over time. It also introduces a new frequency-based method for searching and retrieving specific data from fixed clusters.
Classification of common clustering algorithm and techniques, e.g., hierarchical clustering, distance measures, K-means, Squared error, SOFM, Clustering large databases.
This document provides a short review of clustering techniques for students. It defines clustering and different types of grouping methods such as hard vs soft clustering. It discusses popular clustering algorithms like hierarchical clustering, k-means clustering, and density-based clustering. It also covers cluster validity, usability, preprocessing techniques, meta methods, and visual clustering. Open problems in clustering mentioned include how to identify outlier objects and accelerate classification.
Cluster analysis is an unsupervised learning technique that groups similar data objects into clusters. It finds internal structures within unlabeled data by grouping objects based on their characteristics. Clustering is used to gain insight into data distribution and as a preprocessing step for other algorithms. Some applications of clustering include marketing, land use analysis, insurance risk assessment, and city planning. The quality of clustering depends on how well it separates objects within a cluster from objects in other clusters. Hierarchical clustering creates clusters by iteratively merging or splitting groups of objects based on their distances in a dendrogram.
This document discusses cluster analysis and various clustering algorithms. It begins with an overview of supervised and unsupervised learning, as well as generative models. It then discusses 5 common clustering techniques: partitioning, hierarchical, density-based, grid-based, and model-based clustering. The document also covers challenges with cluster analysis such as centroid initialization, outlier handling, categorical data, the curse of dimensionality, and computational complexity. Specific clustering algorithms discussed in more detail include K-means, K-medoids, K-modes, mini-batch K-means, and scalable K-means++.
This document discusses unsupervised machine learning classification through clustering. It defines clustering as the process of grouping similar items together, with high intra-cluster similarity and low inter-cluster similarity. The document outlines common clustering algorithms like K-means and hierarchical clustering, and describes how K-means works by assigning points to centroids and iteratively updating centroids. It also discusses applications of clustering in domains like marketing, astronomy, genomics and more.
The document provides information about cluster analysis and hierarchical agglomerative clustering. It discusses how hierarchical agglomerative clustering works by successively merging the most similar clusters together based on a distance measure between clusters. It begins with each object as its own cluster and merges them into larger clusters, forming a dendrogram, until all objects are in a single cluster. Different linkage criteria like single, complete, and average linkage can be used to define the distance between clusters.
An Empirical Study for Defect Prediction using Clusteringidescitation
Reliably predicting defects in the software is one of
the holy grails of software engineering. Researchers have
devised and implemented a method of defect prediction
approaches varying in terms of accuracy, complexity, and the
input data they require. An accurate prediction of the number
of defects in a software product during system testing
contributes not only to the management of the system testing
process but also to the estimation of the product’s required
maintenance [1]. A prediction of the number of remaining
defects in an inspected artefact can be used for decision making.
Defective software modules cause software failures, increase
development and maintenance costs, and decrease customer
satisfaction. It strives to improve software quality and testing
efficiency by constructing predictive models from code
attributes to enable a timely identification of fault-prone
modules [2]. In this paper, we will discuss clustering techniques
are used for software defect prediction. This helps the
developers to detect software defects and correct them.
Unsupervised techniques may be used for defect prediction in
software modules, more so in those cases where defect labels
are not available [3].
The document provides an overview of topics to be covered in a data analysis course, including cluster analysis and decision trees. The course will cover descriptive statistics, probability distributions, correlation, regression, hypothesis testing, clustering methods like k-means, and decision tree techniques like CHAID. Clustering involves grouping similar objects together to identify homogeneous clusters that are heterogeneous from each other. Applications of clustering include market segmentation, credit risk analysis, and operations. The document gives an example of clustering students based on their exam scores.
Cluster analysis is a major tool in a number of applications in many fields of Business, Engineering & etc.(The odoridis and Koutroubas, 1999):
Data reduction.
Hypothesis generation.
Hypothesis testing.
Prediction based on groups.
Enhanced Genetic Algorithm with K-Means for the Clustering ProblemAnders Viken
The document proposes a genetic algorithm combined with K-Means clustering to solve clustering problems. The genetic algorithm is used to generate new clusters through crossover, then K-Means is applied to further improve cluster quality and speed up search. Experimental results showed the combined approach converges faster while producing equivalent quality clusters compared to a standard genetic algorithm alone.
Clustering 3d powerpoint ppt templates.SlideTeam.net
The document discusses clustering 3D objects in a slide presentation. Text boxes and shapes are arranged on the slide in a clustered manner. The clusters become increasingly complex with additional text boxes and shapes added in each iteration. Guidance is provided on editing individual elements by ungrouping objects and changing their color. The overall document demonstrates how to build up complex clustered visual arrangements from simple components in PowerPoint.
Cancer data partitioning with data structure and difficulty independent clust...IRJET Journal
This document discusses cancer data partitioning using clustering techniques. It begins with an introduction to clustering concepts and different clustering methods like k-means, hierarchical agglomerative clustering, and partitioning methods. It then reviews literature on clustering algorithms and ensemble methods applied to problems like speaker diarization and tumor clustering from gene expression data. The document analyzes issues with existing clustering methodology and proposes a new dynamic ensemble membership selection scheme to support data structure and complexity independent clustering for cancer data partitioning. The method combines partition around medoids clustering with an incremental semi-supervised cluster ensemble framework to improve healthcare data partitioning accuracy.
Building a Classifier Employing Prism Algorithm with Fuzzy LogicIJDKP
Classification in data mining is receiving immense interest in recent times. As the knowledge is based on
historical data, classifications of data are essential for discovering the knowledge. To decrease the
classification complexity, the quantitative attributes of data need splitting. But the splitting using the
classical logic is less accurate. This can be overcome by the use of fuzzy logic. This paper illustrates how to
build up the classification rules using the fuzzy logic. The fuzzy classifier is built on by using the prism
decision tree algorithm. This classifier produces more realistic results than the classical one. The
effectiveness of this method is justified over a sample dataset.
This document summarizes Chapter 10 of the book "Data Mining: Concepts and Techniques (3rd ed.)" which covers cluster analysis. The chapter introduces different types of clustering methods including partitioning methods like k-means and k-medoids, hierarchical methods, density-based methods, and grid-based methods. It discusses how to evaluate the quality of clustering results and highlights considerations for cluster analysis such as similarity measures, clustering space, and challenges like scalability and high dimensionality.
Cluster analysis is an unsupervised machine learning technique that groups similar data objects into clusters. It finds internal structures within unlabeled data by partitioning it into groups based on similarity. Some key applications of cluster analysis include market segmentation, document classification, and identifying subtypes of diseases. The quality of clusters depends on both the similarity measure used and how well objects are grouped within each cluster versus across clusters.
Textual Data Partitioning with Relationship and Discriminative AnalysisEditor IJMTER
Data partitioning methods are used to partition the data values with similarity. Similarity
measures are used to estimate transaction relationships. Hierarchical clustering model produces tree
structured results. Partitioned clustering produces results in grid format. Text documents are
unstructured data values with high dimensional attributes. Document clustering group ups unlabeled text
documents into meaningful clusters. Traditional clustering methods require cluster count (K) for the
document grouping process. Clustering accuracy degrades drastically with reference to the unsuitable
cluster count.
Textual data elements are divided into two types’ discriminative words and nondiscriminative
words. Only discriminative words are useful for grouping documents. The involvement of
nondiscriminative words confuses the clustering process and leads to poor clustering solution in return.
A variation inference algorithm is used to infer the document collection structure and partition of
document words at the same time. Dirichlet Process Mixture (DPM) model is used to partition
documents. DPM clustering model uses both the data likelihood and the clustering property of the
Dirichlet Process (DP). Dirichlet Process Mixture Model for Feature Partition (DPMFP) is used to
discover the latent cluster structure based on the DPM model. DPMFP clustering is performed without
requiring the number of clusters as input.
Document labels are used to estimate the discriminative word identification process. Concept
relationships are analyzed with Ontology support. Semantic weight model is used for the document
similarity analysis. The system improves the scalability with the support of labels and concept relations
for dimensionality reduction process.
A Novel Approach to Mathematical Concepts in Data Miningijdmtaiir
-This paper describes three different fundamental
mathematical programming approaches that are relevant to
data mining. They are: Feature Selection, Clustering and
Robust Representation. This paper comprises of two clustering
algorithms such as K-mean algorithm and K-median
algorithms. Clustering is illustrated by the unsupervised
learning of patterns and clusters that may exist in a given
databases and useful tool for Knowledge Discovery in
Database (KDD). The results of k-median algorithm are used
to collecting the blood cancer patient from a medical database.
K-mean clustering is a data mining/machine learning algorithm
used to cluster observations into groups of related observations
without any prior knowledge of those relationships. The kmean algorithm is one of the simplest clustering techniques
and it is commonly used in medical imaging, biometrics and
related fields.
The document discusses different clustering techniques used for grouping large amounts of data. It covers partitioning methods like k-means and k-medoids that organize data into exclusive groups. It also describes hierarchical methods like agglomerative and divisive clustering that arrange data into nested groups or trees. Additionally, it mentions density-based and grid-based clustering and provides algorithms for different clustering approaches.
Step by step operations by which we make a group of objects in which attributes
of all the objects are nearly similar, known as clustering. So, a cluster is a collection of
objects that acquire nearly same attribute values. The property of an object in a cluster is
similar to other objects in same cluster but different with objects of other clusters.
Clustering is used in wide range of applications like pattern recognition, image processing,
data analysis, machine learning etc. Nowadays, more attention has been put on categorical
data rather than numerical data. Where, the range of numerical attributes organizes in a
class like small, medium, high, and so on. There is wide range of algorithm that used to
make clusters of given categorical data. Our approach is to enhance the working on well-
known clustering algorithm k-modes to improve accuracy of algorithm. We proposed a new
approach named “High Accuracy Clustering Algorithm for Categorical datasets”.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
This document discusses using genetic programming (GP) for data classification. It begins with an introduction to classification problems and algorithms like decision trees, neural networks, and Bayesian classification. It then provides background on genetic algorithms and genetic programming, explaining that GP uses tree-based representations of computer programs to evolve solutions. The document presents a case study on using GP for classification, discussing fitness measures, creating training sets, and an interleaved data format to address class imbalance. The goal is to automatically generate classification models for data sets using GP.
Enhanced Clustering Algorithm for Processing Online DataIOSR Journals
This document proposes an enhanced incremental clustering algorithm for processing online data. It discusses existing clustering algorithms like leader clustering and hierarchical clustering, which have limitations in handling dynamic data. The proposed algorithm aims to dynamically create initial clusters and rearrange clusters based on data characteristics, allowing for more accurate clustering of online data over time. It also introduces a new frequency-based method for searching and retrieving specific data from fixed clusters.
Classification of common clustering algorithm and techniques, e.g., hierarchical clustering, distance measures, K-means, Squared error, SOFM, Clustering large databases.
This document provides a short review of clustering techniques for students. It defines clustering and different types of grouping methods such as hard vs soft clustering. It discusses popular clustering algorithms like hierarchical clustering, k-means clustering, and density-based clustering. It also covers cluster validity, usability, preprocessing techniques, meta methods, and visual clustering. Open problems in clustering mentioned include how to identify outlier objects and accelerate classification.
Cluster analysis is an unsupervised learning technique that groups similar data objects into clusters. It finds internal structures within unlabeled data by grouping objects based on their characteristics. Clustering is used to gain insight into data distribution and as a preprocessing step for other algorithms. Some applications of clustering include marketing, land use analysis, insurance risk assessment, and city planning. The quality of clustering depends on how well it separates objects within a cluster from objects in other clusters. Hierarchical clustering creates clusters by iteratively merging or splitting groups of objects based on their distances in a dendrogram.
This document discusses cluster analysis and various clustering algorithms. It begins with an overview of supervised and unsupervised learning, as well as generative models. It then discusses 5 common clustering techniques: partitioning, hierarchical, density-based, grid-based, and model-based clustering. The document also covers challenges with cluster analysis such as centroid initialization, outlier handling, categorical data, the curse of dimensionality, and computational complexity. Specific clustering algorithms discussed in more detail include K-means, K-medoids, K-modes, mini-batch K-means, and scalable K-means++.
This document discusses unsupervised machine learning classification through clustering. It defines clustering as the process of grouping similar items together, with high intra-cluster similarity and low inter-cluster similarity. The document outlines common clustering algorithms like K-means and hierarchical clustering, and describes how K-means works by assigning points to centroids and iteratively updating centroids. It also discusses applications of clustering in domains like marketing, astronomy, genomics and more.
The document provides information about cluster analysis and hierarchical agglomerative clustering. It discusses how hierarchical agglomerative clustering works by successively merging the most similar clusters together based on a distance measure between clusters. It begins with each object as its own cluster and merges them into larger clusters, forming a dendrogram, until all objects are in a single cluster. Different linkage criteria like single, complete, and average linkage can be used to define the distance between clusters.
An Empirical Study for Defect Prediction using Clusteringidescitation
Reliably predicting defects in the software is one of
the holy grails of software engineering. Researchers have
devised and implemented a method of defect prediction
approaches varying in terms of accuracy, complexity, and the
input data they require. An accurate prediction of the number
of defects in a software product during system testing
contributes not only to the management of the system testing
process but also to the estimation of the product’s required
maintenance [1]. A prediction of the number of remaining
defects in an inspected artefact can be used for decision making.
Defective software modules cause software failures, increase
development and maintenance costs, and decrease customer
satisfaction. It strives to improve software quality and testing
efficiency by constructing predictive models from code
attributes to enable a timely identification of fault-prone
modules [2]. In this paper, we will discuss clustering techniques
are used for software defect prediction. This helps the
developers to detect software defects and correct them.
Unsupervised techniques may be used for defect prediction in
software modules, more so in those cases where defect labels
are not available [3].
The document provides an overview of topics to be covered in a data analysis course, including cluster analysis and decision trees. The course will cover descriptive statistics, probability distributions, correlation, regression, hypothesis testing, clustering methods like k-means, and decision tree techniques like CHAID. Clustering involves grouping similar objects together to identify homogeneous clusters that are heterogeneous from each other. Applications of clustering include market segmentation, credit risk analysis, and operations. The document gives an example of clustering students based on their exam scores.
Cluster analysis is a major tool in a number of applications in many fields of Business, Engineering & etc.(The odoridis and Koutroubas, 1999):
Data reduction.
Hypothesis generation.
Hypothesis testing.
Prediction based on groups.
Enhanced Genetic Algorithm with K-Means for the Clustering ProblemAnders Viken
The document proposes a genetic algorithm combined with K-Means clustering to solve clustering problems. The genetic algorithm is used to generate new clusters through crossover, then K-Means is applied to further improve cluster quality and speed up search. Experimental results showed the combined approach converges faster while producing equivalent quality clusters compared to a standard genetic algorithm alone.
Clustering 3d powerpoint ppt templates.SlideTeam.net
The document discusses clustering 3D objects in a slide presentation. Text boxes and shapes are arranged on the slide in a clustered manner. The clusters become increasingly complex with additional text boxes and shapes added in each iteration. Guidance is provided on editing individual elements by ungrouping objects and changing their color. The overall document demonstrates how to build up complex clustered visual arrangements from simple components in PowerPoint.
This document provides an overview of Microsoft's Windows clustering technologies, including Network Load Balancing (NLB), Component Load Balancing (CLB), and Server Cluster. It discusses how these technologies can be used together to provide high availability, reliability, and scalability for applications and services. NLB is used for load balancing web servers, CLB balances application components, and Server Cluster provides failover for databases and backend services. Clusters can be organized as farms of identical servers or packs that share partitioned data. Technologies can scale out by adding servers or scale up individual servers. Server Cluster nodes can operate actively or passively.
How to make architecture clustering 2 d powerpoint slides and ppt diagram tem...SlideTeam.net
This document shows a diagram with text boxes clustered in 2D space. The text boxes are arranged in groups with some boxes overlapping or positioned near each other, demonstrating how objects can be clustered together based on their proximity in two-dimensional space.
Load balanced clustering with mimo uploading technique for mobile data gather...Munisekhar Gunapati
in these we three-layers of framework is proposed for mobile data collection in wireless sensor networks which includes the sensor layer, cluster head layer, and mobile collector(called sen car)layer.
the framework employs distributed load balanced clustering and multi data uploading ,which is referred as LBC-MDU .
the objective is to achieve good scalability,long network lifetime,and low data collection latency.
a sensor layer a distributed load balanced clustering algorithm(LBC) is preposed for sensors are self organized themselelves into clusters, in contrast existing clustering methods our scheme generate multiple cluster heads in each cluster to balance the ork load and facilitate multi data uploading .
at clustering head layer the inter-cluster transmission range carefully chosen to guarantee the connectivity among the clusters, multiple clusters heads within a cluster head can cooperate with each other to perform energy-saving inter clustering communication. though inter cluster transimission cluster head information is forwarded to sencar for moving trajectory planning .
at mobile collector layer,sencor equipped with two cluster head ,which enables to simultaneously upload the data into the sencar by using multi-user MIMO technique,the moving trajectory planning for sencar is optimized to fully utilize multi data uploading capability by properly selecting polling points in each cluster ,by visiting each selected polling point, sencar can efficiently gather data from cluster head transport the data static data sink the results show that when each cluster has at most two cluster heads, LBU-MDU achieves over 50 percent energy saving per node and 60 percent energy saving on cluster heads comparing with data collection through multi-hop relay to the static data sink, and 20 percent shorter data collection time compared to traditional mobile data gathering.
Clustering and networking activities are relationship-based activities that support sharing and developing of competences, knowledge and methods. The documents within the toolbox have a clear focus on activities in the area of technology transfer. Networking and clustering activities are critical leverages for all transfer activities presented in this toolbox, namely: opportunities identification, IP management, Human resources and focused value proposition.
www.FITT-for-Innovation.eu
This document provides an overview of SQL Server clustering. It discusses the importance of high availability and introduces some key concepts in clustering like nodes, shared storage, heartbeats, failover and failback. It also covers the basic architecture of a SQL Server cluster, including the virtual server and different types of clusters. Some advantages and disadvantages of clustering are outlined. Finally, it discusses some terminology used in clustering and provides a checklist for preparing Windows clustering.
Compare Clustering Methods for MS SQL ServerAlexDepo
Clustering is very important technology for High Availability and it is important for DBA to understand benefits and pitfalls. With very few available techniques and a lot of gray areas right decision might help to avoid extra costs. Presentation is unveiling clustering basics, reviews and compares clustering technologies including Microsoft, XCOTO Gridscale, and HP PolyserveMatrix. This presentation can be helpful not only to beginners but to intermediate level DBAs and infrastructure managers.
Weblogic-clustering-failover-and-load-balancing-trainingUnmesh Baile
The document discusses WebLogic clustering, which allows for failover and load balancing of WebLogic server instances. It provides an introduction to clustering and its benefits like high-availability and scalability. The document then describes WebLogic clustering specifically, including the types of objects that can be clustered. It discusses load balancing algorithms and shows the results of testing a clustered WebLogic environment with round-robin, weight-based, and random algorithms. The round-robin algorithm proved most effective with improved scalability when adding a second node to the cluster.
This document provides an overview of clustering techniques. It defines clustering as grouping a set of similar objects into classes, with objects within a cluster being similar to each other and dissimilar to objects in other clusters. The document then discusses partitioning, hierarchical, and density-based clustering methods. It also covers mathematical elements of clustering like partitions, distances, and data types. The goal of clustering is to minimize a similarity function to create high similarity within clusters and low similarity between clusters.
Application of Clustering in Data Science using Real-life Examples Edureka!
This document outlines an Edureka webinar on applications of clustering in real life. The webinar instructor is Kumaran Ponnambalam. The objectives are to understand data science applications and prospects, machine learning categories, clustering and k-means clustering. Examples of clustering applications include wine recommendation, pizza delivery optimization, and news summarization. K-means clustering is demonstrated on pizza delivery location data. The webinar also discusses data science job trends and covers 10 modules on data science topics including machine learning techniques in R.
- The document discusses the basics of Windows clustering and quorum models. It defines what clustering is and why it is used for high availability of critical applications.
- It covers the differences between Windows 2008 and 2012 clustering capabilities. Windows 2012 supports more nodes, built-in iSCSI, and file/storage services.
- Common clustering terms are defined like active/passive, heartbeat network, shared disks, quorum, cluster resources, and groups.
- Quorum configuration options are reviewed including typical settings, adding witnesses, and advanced configurations. Quorum helps ensure only one cluster remains active if communication is lost.
This document outlines clustering algorithms for large datasets. It discusses k-means clustering and extensions like k-means++ that improve initialization. It also covers spectral relaxation methods that reformulate k-means as a trace maximization problem to address local minima. Additionally, it proposes landmark-based clustering algorithms for biological sequences that select landmarks in one pass and assign sequences to the nearest landmark using hashing to search for neighbors. The document provides analysis of the time and space complexity of these algorithms as well as assumptions about separability and cluster size.
K-means clustering is an algorithm that groups data points into k number of clusters based on their similarity. It works by randomly selecting k data points as initial cluster centroids and then assigning each remaining point to the closest centroid. It then recalculates the centroids and reassigns points in an iterative process until centroids stabilize. While efficient, k-means clustering has weaknesses in that it requires specifying k, can get stuck in local optima, and is not suitable for non-convex shaped clusters or noisy data.
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A SURVEY ON OPTIMIZATION APPROACHES TO TEXT DOCUMENT CLUSTERINGijcsa
This document provides a survey of optimization approaches that have been applied to text document clustering. It discusses several clustering algorithms and categorizes them as partitioning methods, hierarchical methods, density-based methods, grid-based methods, model-based methods, frequent pattern-based clustering, and constraint-based clustering. It then describes several soft computing techniques that have been used as optimization approaches for text document clustering, including genetic algorithms, bees algorithms, particle swarm optimization, and ant colony optimization. These optimization techniques perform a global search to improve the quality and efficiency of document clustering algorithms.
Particle Swarm Optimization based K-Prototype Clustering Algorithm iosrjce
This document summarizes a research paper that proposes a new Particle Swarm Optimization (PSO) based K-Prototype clustering algorithm to cluster mixed numeric and categorical data. It begins with background information on clustering algorithms like K-Means, K-Modes, and K-Prototype. It then describes the K-Prototype algorithm, PSO, and discrete binary PSO. Related work integrating PSO with other clustering algorithms is also reviewed. The proposed approach uses binary PSO to select improved initial prototypes for K-Prototype clustering in order to obtain better clustering results than traditional K-Prototype and avoid local optima.
This document discusses using particle swarm optimization to improve the k-prototype clustering algorithm. The k-prototype algorithm clusters data with both numeric and categorical attributes but can get stuck in local optima. The proposed method uses particle swarm optimization, a global optimization technique, to guide the k-prototype algorithm towards better clusterings. Particle swarm optimization models potential solutions as particles that explore the search space. It is integrated with k-prototype clustering to avoid locally optimal solutions and produce better clusterings. The method is tested on standard benchmark datasets and shown to outperform traditional k-modes and k-prototype clustering algorithms.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
This document discusses multidimensional clustering methods for data mining and their industrial applications. It begins with an introduction to clustering, including definitions and goals. Popular clustering algorithms are described, such as K-means, fuzzy C-means, hierarchical clustering, and mixture of Gaussians. Distance measures and their importance in clustering are covered. The K-means and fuzzy C-means algorithms are explained in detail. Examples are provided to illustrate fuzzy C-means clustering. Finally, applications of clustering algorithms in fields such as marketing, biology, and earth sciences are mentioned.
An Heterogeneous Population-Based Genetic Algorithm for Data Clusteringijeei-iaes
As a primary data mining method for knowledge discovery, clustering is a technique of classifying a dataset into groups of similar objects. The most popular method for data clustering K-means suffers from the drawbacks of requiring the number of clusters and their initial centers, which should be provided by the user. In the literature, several methods have proposed in a form of k-means variants, genetic algorithms, or combinations between them for calculating the number of clusters and finding proper clusters centers. However, none of these solutions has provided satisfactory results and determining the number of clusters and the initial centers are still the main challenge in clustering processes. In this paper we present an approach to automatically generate such parameters to achieve optimal clusters using a modified genetic algorithm operating on varied individual structures and using a new crossover operator. Experimental results show that our modified genetic algorithm is a better efficient alternative to the existing approaches.
This document compares four clustering algorithms (K-means, hierarchical, EM, and density-based) using the WEKA tool. It applies the algorithms to a dataset of software classes and evaluates them based on number of clusters, time to build models, squared errors, and log likelihood. The results show that K-means performs best in terms of time to build models, while density-based clustering performs best in terms of log likelihood. Overall, the document concludes that K-means is the best algorithm for this dataset because it balances low runtime and good clustering accuracy.
Ensemble based Distributed K-Modes ClusteringIJERD Editor
Clustering has been recognized as the unsupervised classification of data items into groups. Due to the explosion in the number of autonomous data sources, there is an emergent need for effective approaches in distributed clustering. The distributed clustering algorithm is used to cluster the distributed datasets without gathering all the data in a single site. The K-Means is a popular clustering method owing to its simplicity and speed in clustering large datasets. But it fails to handle directly the datasets with categorical attributes which are generally occurred in real life datasets. Huang proposed the K-Modes clustering algorithm by introducing a new dissimilarity measure to cluster categorical data. This algorithm replaces means of clusters with a frequency based method which updates modes in the clustering process to minimize the cost function. Most of the distributed clustering algorithms found in the literature seek to cluster numerical data. In this paper, a novel Ensemble based Distributed K-Modes clustering algorithm is proposed, which is well suited to handle categorical data sets as well as to perform distributed clustering process in an asynchronous manner. The performance of the proposed algorithm is compared with the existing distributed K-Means clustering algorithms, and K-Modes based Centralized Clustering algorithm. The experiments are carried out for various datasets of UCI machine learning data repository.
Literature Survey On Clustering TechniquesIOSR Journals
This document provides a literature review of different clustering techniques. It begins by defining clustering and describing the main categories of clustering methods: hierarchical, partitioning, density-based, grid-based, and model-based. It then summarizes some examples of algorithms for each category in 1-2 sentences. For hierarchical methods, it discusses BIRCH, CURE, and CHAMELEON. For partitioning methods, it mentions k-means clustering and k-medoids. For density-based methods, it lists DBSCAN, OPTICS, DENCLUE. For grid-based methods, it lists CLIQUE, STING, MAFIA, WAVE CLUSTER, O-CLUSTER, ASGC, and
Incremental learning from unbalanced data with concept class, concept drift a...IJDKP
Recently, stream data mining applications has drawn vital attention from several research communities.
Stream data is continuous form of data which is distinguished by its online nature. Traditionally, machine
learning area has been developing learning algorithms that have certain assumptions on underlying
distribution of data such as data should have predetermined distribution. Such constraints on the problem
domain lead the way for development of smart learning algorithms performance is theoretically verifiable.
Real-word situations are different than this restricted model. Applications usually suffers from problems
such as unbalanced data distribution. Additionally, data picked from non-stationary environments are also
usual in real world applications, resulting in the “concept drift” which is related with data stream
examples. These issues have been separately addressed by the researchers, also, it is observed that joint
problem of class imbalance and concept drift has got relatively little research. If the final objective of
clever machine learning techniques is to be able to address a broad spectrum of real world applications,
then the necessity for a universal framework for learning from and tailoring (adapting) to, environment
where drift in concepts may occur and unbalanced data distribution is present can be hardly exaggerated.
In this paper, we first present an overview of issues that are observed in stream data mining scenarios,
followed by a complete review of recent research in dealing with each of the issue.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
The document discusses clustering and its applications in contour detection. It notes that while clustering is widely used to organize unlabeled data and remove noise, there are still challenges. Specifically, selecting an appropriate data set, determining the number of clusters, and validating results can be ambiguous. Clustering algorithms are also sensitive to these parameters and the data set properties. Contour extraction methods also lack efficiency and universality. Improved clustering techniques are needed that can be more effectively applied to contour detection problems across different data sets.
An Analysis On Clustering Algorithms In Data MiningGina Rizzo
This document summarizes and analyzes various clustering algorithms used in data mining. It begins with an introduction to clustering and discusses hierarchical and partitional clustering approaches. Specific algorithms discussed include k-means, single linkage, and fuzzy clustering. The document compares techniques on aspects like complexity and sensitivity. It concludes that choosing the best clustering algorithm depends on factors like the data type, size, and desired cluster structure. The ideal is to have criteria to automatically select the appropriate algorithm for a given data set.
Extended pso algorithm for improvement problems k means clustering algorithmIJMIT JOURNAL
The clustering is a without monitoring process and one of the most common data mining techniques. The
purpose of clustering is grouping similar data together in a group, so were most similar to each other in a
cluster and the difference with most other instances in the cluster are. In this paper we focus on clustering
partition k-means, due to ease of implementation and high-speed performance of large data sets, After 30
year it is still very popular among the developed clustering algorithm and then for improvement problem of
placing of k-means algorithm in local optimal, we pose extended PSO algorithm, that its name is ECPSO.
Our new algorithm is able to be cause of exit from local optimal and with high percent produce the
problem’s optimal answer. The probe of results show that mooted algorithm have better performance
regards as other clustering algorithms specially in two index, the carefulness of clustering and the quality
of clustering.
A h k clustering algorithm for high dimensional data using ensemble learningijitcs
The document summarizes a proposed clustering algorithm for high dimensional data that combines hierarchical (H-K) clustering, subspace clustering, and ensemble clustering. It begins with background on challenges of clustering high dimensional data and related work applying dimension reduction, subspace clustering, ensemble clustering, and H-K clustering individually. The proposed model first applies subspace clustering to identify clusters within subsets of features. It then performs H-K clustering on each subspace cluster. Finally, it applies ensemble clustering techniques to integrate the results into a single clustering. The goal is to leverage each technique's strengths to improve clustering performance for high dimensional data compared to using a single approach.
It is a data mining technique used to place the data elements into their related groups. Clustering is the process of partitioning the data (or objects) into the same class, The data in one class is more similar to each other than to those in other cluster.
Extended pso algorithm for improvement problems k means clustering algorithmIJMIT JOURNAL
The clustering is a without monitoring process and one of the most common data mining techniques. The
purpose of clustering is grouping similar data together in a group, so were most similar to each other in a
cluster and the difference with most other instances in the cluster are. In this paper we focus on clustering
partition k-means, due to ease of implementation and high-speed performance of large data sets, After 30
year it is still very popular among the developed clustering algorithm and then for improvement problem of
placing of k-means algorithm in local optimal, we pose extended PSO algorithm, that its name is ECPSO.
Our new algorithm is able to be cause of exit from local optimal and with high percent produce the
problem’s optimal answer. The probe of results show that mooted algorithm have better performance
regards as other clustering algorithms specially in two index, the carefulness of clustering and the quality
of clustering.
The improved k means with particle swarm optimizationAlexander Decker
This document summarizes a research paper that proposes an improved K-means clustering algorithm using particle swarm optimization. It begins with an introduction to data clustering and types of clustering algorithms. It then discusses K-means clustering and some of its drawbacks. Particle swarm optimization is introduced as an optimization technique inspired by swarm behavior in nature. The proposed algorithm uses particle swarm optimization to select better initial cluster centroids for K-means clustering in order to overcome some limitations of standard K-means. The algorithm works in two phases - the first uses particle swarm optimization and the second performs K-means clustering using the outputs from the first phase.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document summarizes a research paper that proposes a new density-based clustering technique called Triangle-Density Based Clustering Technique (TDCT) to efficiently cluster large spatial datasets. TDCT uses a polygon approach where the number of data points inside each triangle of a polygon is calculated to determine triangle densities. Triangle densities are used to identify clusters based on a density confidence threshold. The technique aims to identify clusters of arbitrary shapes and densities while minimizing computational costs. Experimental results demonstrate the technique's superiority in terms of cluster quality and complexity compared to other density-based clustering algorithms.
A Density Based Clustering Technique For Large Spatial Data Using Polygon App...IOSR Journals
This document presents a density-based clustering technique called TDCT (Triangle-density based clustering technique) for efficiently clustering large spatial datasets. The technique uses a polygon approach where the number of data points inside each triangle of a polygon is calculated. If the ratio of point densities between two neighboring triangles exceeds a threshold, the triangles are merged into the same cluster. The technique is capable of identifying clusters of arbitrary shapes and densities. Experimental results demonstrate the technique has superior cluster quality and complexity compared to other methods.
Similar to Multilevel techniques for the clustering problem (20)
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5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
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Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
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Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
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7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
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9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
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But designing a data warehouse correctly is a hard task, which requires gathering information about the business processes that need to be analysed in the first place. These processes must be translated into so-called star schemas, which means, denormalised databases where each table represents a dimension or facts.
We will discuss these topics:
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- Types of dimensions;
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Computer Science & Information Technology (CS & IT)
and challenging problems due to its unsupervised nature. It is important to make a distinction
between supervised classification and unsupervised clustering.
In supervised classification, the analyst has available sufficient knowledge to generate
representative parameters for each class of interest. This phase is referred to as training. Once
trained, a chosen classifier is then used to attach labels to all objects according to the trained
parameters. In the case of clustering analysis, a clustering algorithm is used to build a knowledge
structure by using some measure of cluster quality to group objects in classes. The primary goal is
to discover concepts structure in data objects. The paper is organized as follows: Section 2
presents a short survey of techniques for the clustering problem. Section 3 explains the clustering
problem while Section 4 describes the genetic algorithm and the K-Means algorithm. Section 5
introduces the multilevel paradigm , while section 6 presents the experimental results. Finally,
Section 7 presents a summary and possible future work.
2. A SHORT SURVEY OF ALGORITHM
Cluster analysis has been a hot topic of research due to its applicability in many disciplines
including market segmentation [3], image processing [4], web mining [5], and bio-informatics [6]
to name just a few. This has stimulated the search for efficient clustering approximation
algorithms which can be broadly be divided into three main types: hierarchical, partitional, and
local search methods. Hierarchical clustering algorithms [7] construct a hierarchy of clusters
using either agglomerative or divisive style. The agglomerative style starts with each data object
in its own cluster, and at each step, the closest pair of clusters are merged using a metric of cluster
proximity. Different agglomerative algorithms differ in how the clusters are merged at each level.
With divisive clustering, all data objects are initially placed in one cluster and clusters are
repeatedly split in two until all data objects are in their own cluster. On the other hand, Nonhierarchical or partitional clustering [8] are based on iterative relocation of data objects between
clusters. The set of data objects is divided into non-overlapping clusters such that each data object
lies in exactly one cluster. The quality of the solution is measured by a clustering criterion. At
each iteration, the algorithm improves the value of the criterion function until convergence is
reached. The algorithms belonging to this class generate solutions from scratch by adding to an
initially empty partial solution components, until a solution is complete. They are regarded as the
fastest approximate methods, yet they often return solutions of inferior quality. Finally, local
search methods constitute an alternative to the traditional partitional techniques. These techniques
offer the advantage of being flexible. They can be applied to any problem (discrete or continuous)
whenever there is a possibility for encoding a candidate solution to the problem, and a mean of
computing the quality of any candidate solution through the so-called cost function. They have
the advantage that they could escape more efficiently from local minima. They start from some
initial solution and iteratively try to replace the current solution by a better one in the light of the
cost function in an appropriately defined neighbourhood of the current solution. Their
performances depend highly on finding a tactical interplay between diversification and
intensification. The former refers to the ability to explore many different regions of the search
space, whereas the latter refers to the ability to obtain high quality solutions within those regions.
Examples include genetic algorithms [9] [10], Tabu Search [11], Grasp [12].
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3. THE CLUSTERING PROBLEM
The clustering Problem can be defined as follows: Given a finite set of N data objects where
each object is a finite set of attributes or feature from which it can be identified. A relation
defining the constraints on the resulting clusters. The relation to be respected by all the formed
clusters is that no pairs of clusters should have a data object in common. A solution to the
clustering problem requires the partitioning of the N data objects into a set of K clusters such
that objects in the same cluster are more similar to each other than to those in other clusters.
Searching all possible clustering alternatives would not be possible. Because of this reason, there
is a considerable interest in the design of heuristics to solve the clustering problems using a cost
function that quantifies the goodness of the clusters on the basis of the similarity or dissimilarity
measures of the data objects. A commonly used cost function is the sum of squared distances of
the data objects to their cluster representatives. Euclidean distance is the most widely used
distance function in the clustering context.
4. ALGORITHMS
4.1 Genetic Algorithms
Genetic Algorithms [13] are stochastic methods for global search and optimization and belong to
the group of Evolutionary Algorithms. They simultaneously examines and manipulates a set of
possible solution. Given a specific problem to solve, the input to GAs is an initial population of
solutions called individuals or chromosomes. A gene is part of a chromosome, which is the
smallest unit of genetic information. Every gene is able to assume different values called allele.
All genes of an organism form a genome which affects the appearance of an organism called
phenotype. The chromosomes are encoded using a chosen representation and each can be
thought of as a point in the search space of candidate solutions. Each individual is assigned a
score (fitness) value that allows assessing its quality. The members of the initial population may
be randomly generated or by using sophisticated mechanisms by means of which an initial
population of high quality chromosomes is produced. The reproduction operator selects
(randomly or based on the individual's fitness) chromosomes from the population to be parents
and enters them in a mating pool. Parent individuals are drawn from the mating pool and
combined so that information is exchanged and passed to off-springs depending on the
probability of the cross-over operator. The new population is then subjected to mutation and
enters into an intermediate population. The mutation operator acts as an element of diversity into
the population and is generally applied with a low probability to avoid disrupting cross-over
results. Finally, a selection scheme is used to update the population giving rise to a new
generation. The individuals from the set of solutions which is called population will evolve from
generation to generation by repeated applications of an evaluation procedure that is based on
genetic operators. Over many generations, the population becomes increasingly uniform until it
ultimately converges to optimal or near-optimal solutions. Below are the various steps used in the
proposed genetic algorithm.
4.1.1 Fitness function
The notion of fitness is fundamental to the application of genetic algorithms. It is a numerical
value that expresses the performance of an individual (solution) so that different individuals can
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be compared. The fitness function used by the genetic algorithm is simply the Euclidean
distance.
4.1.2 Representation
A representation is a mapping from the state space of possible solutions to a state of encoded
solutions within a particular data structure. The encoding scheme used in this work is based on
integer encoding. An individual or chromosome is represented using a vector of n positions,
where n is the set of data objects. Each position corresponds to a particular data object, i.e, he ith
position (gene) represents the ith data object. Each gene has a value over the set {1,2,....k}. These
values define the set of cluster labels.
4.1.3 Initial population
The initial population consists of individuals generated randomly in which each gene's allele is
assigned randomly a label from the set of cluster labels.
4.1.4 Cross-over
The task of the cross-over operator is to reach regions of the search space with higher average
quality. New solutions are created by combining pairs of individuals in the population and then
applying a crossover operator to each chosen pair. The individuals are visited in random order.
An unmatched individual i_l is matched randomly with an unmatched individual i_m. Thereafter,
the two-point crossover operator is applied using a cross-over probability to each matched pair of
individuals. The two-point crossover selects two randomly points within a chromosome and then
interchanges the two parent chromosomes between these points to generate two new offspring.
Recombination can be defined as a process in which a set of configurations (solutions referred as
parents) undergoes a transformation to create a set of configurations (referred as off-springs). The
creation of these descendants involves the location and combinations of features extracted from
the parents. The reason behind choosing the two point crossover are the results presented in
cite{crossover} where the difference between the different crossovers are not significant when
the problem to be solved is hard. In addition, the work conducted in [14] shows that the twopoint crossover is more effective when the problem at hand is difficult to solve.
4.1.5 Mutation
The purpose of mutation which is the secondary search operator used in this work, is to generate
modified individuals by introducing new features in the population. By mutation, the alleles of
the produced child individuals have a chance to be modified, which enables further exploration
of the search space. The mutation operator takes a single parameter p_m, which specifies the
probability of performing a possible mutation. Let I {c_1,c_2, …. ,c_k} be an individual where
each of whose gene c_i is a cluster label. In our mutation operator, each gene c_i is mutated
through flipping this gene's allele from the current cluster label c_i to a new randomly chosen
cluster label if the probability test is passed. The mutation probability ensures that, theoretically,
every region of the search space is explored. The mutation operator prevents the searching
process from being trapped into local optima while adding to the diversity of the population and
thereby increasing the likelihood that the algorithm will generate individuals with better fitness
values.
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4.1.6 Selection
The selection operator acts on individuals in the current population. During this phase, the search
for the global solution gets a clearer direction, whereby the optimization process is gradually
focused on the relevant areas of the search space. Based on each individual fitness, it determines
the next population. In the roulette method , the selection is stochastic and biased towards the best
individuals. The first step is to calculate the cumulative fitness of the whole population through
the sum of the fitness of all individuals. After that, the probability of selection is calculated for
each individual.
4.2 K-Means Algorithm
The K-means [15] is a simple and well known algorithm used for solving the clustering problem.
The goal of the algorithm is to find the best partitioning of N objects into K clusters , so that the
total distance between the cluster's members and its corresponding centroid, representative of the
cluster is minimized. The algorithm uses an iterative refinement strategy using the following
steps:
1) This step determines the starting cluster's centroids. A very common used strategy is to
assign random k different objects as being the centroids.
2) Assign each object to the cluster that has the closest centroid. In order to find the cluster
with the most similar centroid, the algorithm must calculate the distance between all the
objects and each centroid.
3) Recalculate the values of the centroids. The values of the centroid are updated by taking
as the average of the values of the object's attributes that are part of the cluster.
4) Repeat steps 2 and 3 iteratively until objects can no longer change clusters.
5. THE MULTILEVEL PARADIGM
The multilevel paradigm [16] is a simple technique which at its core involves recursive
coarsening to produce smaller and smaller problems that are easier to solve than the original one.
The multilevel paradigm consists of four phases: coarsening, initial solution, projection and
refinement. The coarsening phase aims at merging the variables associated with the problem to
form clusters. The clusters are used in a recursive manner to construct a hierarchy of problems
each representing the original problem but with fewer degrees of freedom. This phase is repeated
until the size of the smallest problem falls below a specified reduction threshold. Then, a solution
for the problem at the coarsest level is generated, and then successively projected back onto each
of the intermediate levels in reverse order. The solution at each child level is improved before
moving to the parent level. A common feature that characterizes multilevel algorithms, is that any
solution in any of the coarsened problems is a legitimate solution to the original problem. The
multilevel paradigm comprises the following steps:
5.1 Reduction Phase:
The first component in the multilevel framework is the so-called coarsening or reduction phase.
Let P_0 (the subscript represents the level of problem scale) be the set of data objects to be
clustered. The next coarser level P_1 is constructed from P_0 using two different algorithms. The
first algorithm is a random coarsening scheme (RC) The data objects are visited in a random
order. If a data object O_i has not been matched yet, then a randomly unmatched data object O_j
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is selected, and a new data objects O_k (a cluster) consisting of the two data objects O_i and O_j
is created. The set of attributes of the new data object O_k is calculated by taking the average of
each attribute from O_i and its corresponding one from O_j. Unmerged data objects are simply
copied to the next level. The second coarsening algorithm distance coarsening (MC) exploits a
measure of the connection strength between the data object which relies on the notion of distance.
The data objects are visited in a random order. However, instead of merging a data object O_i
with a random object O_j, the data object O_i is merged with O_m such that Euclidean distance
function is minimized. The new formed data objects are used to define a new and smaller
problem and recursively iterate the reduction process until the size of the problem reaches some
desired threshold .
5.2 Initial Clustering
The reduction phase ceases when the problem size shrinks to a desired threshold. Initialization is
then trivial and consists of generating an initial clustering ( S_m) for the problem using a
random procedure. The clusters of every individual in the population are assigned a random
label from the set of cluster labels.
5.3 Projection Phase
The projection phase refers to the inverse process followed during the reduction phase. Having
improved the quality of the clustering on level S_{m+1}, this clustering must be extended on is
parent level S_m. The extension algorithm is simple; if a data object O_i in S_{m+1} is
assigned the cluster label c_l, then the merged pair of data objects that it represents, O_l, O_m
in S_m are also assigned the cluster label c_l .
5.4 Improvement or Refinement Phase
The idea behind the improvement phase is to use the projected clustering at level S_{m+1} as
the initial clustering for the level S_m for further refinement using GA or K-Means described in
the previous section. Even though the clustering at the level S_{m+1} is at a local minimum, the
projected clustering may not be at a local optimum with respect to S_m. The projected clustering
is already a good solution and contains individuals with low function value, GA and K-means
will converge quicker to a better clustering. As soon as the population tends to loose its
diversity, premature convergence occurs and all individuals in the population tend to be identical
with almost the same fitness value. During each level, the genetic algorithm is assumed to reach
convergence when no further improvement of the best solution has not been made during five
consecutive generations.
6. EXPERIMENTAL RESULTS
6.1 Benchmark Instances and Parameter Settings
The performance of the multilevel paradigm is compared against its single variant using a set of
instances taken from real industrial problems. This set is taken from the Machine Learning
Repository website (http://archive.ics.uci.edu/ml/datasets). Due to the randomization nature of the
algorithms, each problem instance was run 100 times. The tests were carried out on a DELL
machine with 800 MHz CPU and 2 GB of memory. The code was written in C and compiled with
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43
the GNU C compiler version 4.6. The following parameters have been fixed experimentally and
are listed below:
-Crossover probability = 0.85
-Mutation probability = 0.01
-Population size = 50
-Stopping criteria for the reduction phase: The reduction process stops as soon as the size of the
coarsest problem reaches 10 % the size of the original problem.
-Convergence during the refinement phase: If there is no observable improvement of the cost
Euclidean distance cost function during 5 consecutive generations (GA) or iterations (for KMeans), both algorithms are assumed to have reached convergence and the improvement phase is
moved to a higher level
6.2 Analysis of Results
The plot in Figures [1]-[8] show the evolution of the cost function versus the quality of the
clustering. The plots suggest that cluster problem solving with GA happens in two phases. In the
first phase, the cost function decreases rapidly and then flattens off as we reach the plateau
region, marking the start of the second phase. The plateau region spans a region in the search
space where the best value of the cost function remains unchanged. The plateau region may be of
a short length depending on whether the algorithm possesses some sort of mechanisms capable of
escaping from it, otherwise the algorithm stagnates and a premature convergence of the algorithm
is detected. A closer look at Figures 1-2 show that the quality of the clustering reaches its highest
value at 0.88% and continues to get marginally worse (0.87%) while the value of cost function is
slightly decreasing. The plots depicted in Figures 3-4 show that the quality of the clustering drops
from 0.80% to 0.79% before GA enters a premature convergence state. On the other hand, the
curve of the cost function continues to decrease showing some improvement. An improvement of
37% in the cost function led to no improvement in the quality of the clustering. The same
phenomenon is detected with K-Means algorithm. The plots at Figures 5-6 reveal that the quality
of the clustering is at its maximum value (0.91%) and suddenly get worse by almost 6% while
the cost function is showing an improvement by a factor of 7%. Finally the plots at Figures 7-8
confirm that the quality of the clustering is getting worse after reaching a peak at 0.91while the
cost function is indicating the opposite and attaining slightly low values. These observations
demonstrate that the cost function scores do not capture the quality of the clustering making it an
unsuitable metric to apply for maximizing both the homogeneity within each cluster and the
heterogeneity between different clusters. Figures 9-11 show the impact of the two coarsening
schemes on the final cost function score. In most cases, the curve of MC remains lower compared
to RC during the different levels and may reach the same level as RC or maintains its superiority
until GA converges. The main conclusion that may be drawn from these plots is that MC is at
least as good as RC or better as it provides a lower cost function value. The experimental results
demonstrated the K-Means combined with MC delivers better clustering than K-Means with RC
in 4 out of the 8 cases (up to 11%), similar results in one case and does worse in two cases ( up
to 3%) while requiring between 15% and 55% more time. When GA is considered, MC
outperforms RC in 3 cases (up to 24%), while it performs 2%worse in only one case. The time of
GA combined with MC ranges from 2% to 19% of the time of GA combined with RC.
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Comparing the two multilevel algorithms using MC as the chosen coarsening scheme, MLVLGA produces better quality in 3 out of 8 cases and the difference in quality ranges from 2% to
24%. For the remaining 3 cases where MLVL-K-Means does better, the improvement is only
marginally better (between 0.9% and 2%). Looking at the time spent MLVL-K-Means, in all the
cases requires the least amount of time (up to 99% faster).With regard to the multilevel paradigm,
it is somewhat unsatisfactory that its ability to enhance the convergence behavior of the two
algorithms is not conclusive. However, This does not seem to be in line with with the general
success established in other combinatorial optimization problems such as the graph partitioning
problem [16] and the satisfiability problem [17]. The reason behind this sort of convergence
behaviour observed in the multilevel paradigm is not obvious but we can speculate. As pointed
earlier, the multilevel paradigm requires that any solution in any of the coarsened problems
should induce a legitimate solution on the original problem. Thus at any stage after initialisation
the current solution could simply be extended through all the problem levels to achieve a solution
of the original problem. This requirement is violated in our case. The attributes of each object
formed during each child level are calculated by taking the average of the attributes of two
different objects from the parent level. The consequence of this procedure is that the optimization
is carried out on different levels each having its own space. The clustering obtained at the coarse
space and the original space do not have have the same cost with respect to the objective
function.
Figure 1. Average development for 100 runs. Evolution of the Euclidean cost function for BreastCanser
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Figure 2. Average Development for 100 Runs Evolution of the Quality of the Clustering for BreastCancer
Figure 3. Average Development for 100 Runs. Evolution of Euclidean Cost Function for Hepatitis
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Figure 4. Average Development for 100 Runs. Evolution of the Quality of the Clutering for Hepatitis
Figure 5. Average Development for 100 Runs. Evolution of Euclidean Cost Function for Breast.
11. Computer Science & Information Technology (CS & IT)
Figure 6. Average Development for 100 Runs. Evolution of the Quality of the Clustering for Breast.
Figure 7. Average Development for 100 Runs. Evolution of Euclidean Cost Function for IRISIS
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Figure 8. Average Development for 100 Runs. Evolution of the Quality of the Clustering for IRISIS.
Figure 9. Comparison of Coarsening Schemes for Glass Figure
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Figure 10. Comparison of Coarsening Schemes for Breastcancer
Figure 11. Comparison of Coarsening Schemes for Hepatitis
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7. CONCLUSIONS
This paper introduces a multilevel scheme combined with the popular K-Means and genetic
algorithm for the clustering problem. The first conclusion drawn from the results at least for the
instances tested in this work generally indicate that the Euclidean Distance cost function widely
used in literature does not capture the quality of the clustering making it an unsuitable metric to
apply for maximizing both the homogeneity within each cluster and the heterogeneity between
different clusters. The coarsening methods used during the coarsening phase have a great impact
on the quality of the clustering. The quality of the clustering provided by MC is at least as good
or better compared to RC regardless of which algorithm is used during the refinement phase. To
summarise then, the multilevel paradigm can improve the asymptotic convergence of the original
algorithms. An obvious subject for further work would be the use of different cost functions and
better coarsening schemes so that the algorithms used during the refinement phase work on
identical search spaces. A better coarsening strategy would be to let the merged objects during
each level be used to create coarser problems so that each entity of a coarse problem P_k is
composed of 2^k objects. The adopted strategy will provide K-Means and GA to work on
identical search spaces during the refinement phase.
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AUTHOR
Master Thesis from University of Bergen , Norway, PhD Thesis in Computer Science
from the University of Neuchatel in Switzerland. His research interests include MetaHeuristics, Parallel Computing, Data Minning.