The document presents a new feature selection algorithm called FAST (Feature Cluster-based Subset Selection) that aims to efficiently reduce dimensionality by removing irrelevant and redundant features. The FAST algorithm works in two steps: (1) it clusters features using graph theoretic methods, and (2) it selects the most representative feature from each cluster. This clustering-based approach has a high probability of selecting useful and independent features. The algorithm is evaluated on high dimensional datasets and shown to improve learning accuracy while reducing dimensionality compared to other feature selection methods.
C LUSTERING B ASED A TTRIBUTE S UBSET S ELECTION U SING F AST A LGORITHmIJCI JOURNAL
In machine learning and data mining, attribute sel
ect is the practice of selecting a subset o
f most
consequential attributes for utilize in model const
ruction. Using an attribute select method is that t
he data
encloses many redundant or extraneous attributes. W
here redundant attributes are those which sup
ply
no supplemental information than the presently
selected attributes, and impertinent attribut
es offer
no valuable information in any context
A fast clustering based feature subset selection algorithm for high-dimension...IEEEFINALYEARPROJECTS
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DOTNET 2013 IEEE CLOUDCOMPUTING PROJECT A fast clustering based feature subse...IEEEGLOBALSOFTTECHNOLOGIES
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EFFICIENT FEATURE SUBSET SELECTION MODEL FOR HIGH DIMENSIONAL DATAIJCI JOURNAL
This paper proposes a new method that intends on reducing the size of high dimensional dataset by
identifying and removing irrelevant and redundant features. Dataset reduction is important in the case of
machine learning and data mining. The measure of dependence is used to evaluate the relationship
between feature and target concept and or between features for irrelevant and redundant feature removal.
The proposed work initially removes all the irrelevant features and then a minimum spanning tree of
relevant features is constructed using Prim’s algorithm. Splitting the minimum spanning tree based on the
dependency between features leads to the generation of forests. A representative feature from each of the
forests is taken to form the final feature subset
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A fast clustering based feature subset selection algorithm for high-dimension...IEEEFINALYEARPROJECTS
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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.
C LUSTERING B ASED A TTRIBUTE S UBSET S ELECTION U SING F AST A LGORITHmIJCI JOURNAL
In machine learning and data mining, attribute sel
ect is the practice of selecting a subset o
f most
consequential attributes for utilize in model const
ruction. Using an attribute select method is that t
he data
encloses many redundant or extraneous attributes. W
here redundant attributes are those which sup
ply
no supplemental information than the presently
selected attributes, and impertinent attribut
es offer
no valuable information in any context
A fast clustering based feature subset selection algorithm for high-dimension...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
DOTNET 2013 IEEE CLOUDCOMPUTING PROJECT A fast clustering based feature subse...IEEEGLOBALSOFTTECHNOLOGIES
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EFFICIENT FEATURE SUBSET SELECTION MODEL FOR HIGH DIMENSIONAL DATAIJCI JOURNAL
This paper proposes a new method that intends on reducing the size of high dimensional dataset by
identifying and removing irrelevant and redundant features. Dataset reduction is important in the case of
machine learning and data mining. The measure of dependence is used to evaluate the relationship
between feature and target concept and or between features for irrelevant and redundant feature removal.
The proposed work initially removes all the irrelevant features and then a minimum spanning tree of
relevant features is constructed using Prim’s algorithm. Splitting the minimum spanning tree based on the
dependency between features leads to the generation of forests. A representative feature from each of the
forests is taken to form the final feature subset
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
A fast clustering based feature subset selection algorithm for high-dimension...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
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.
A Survey on Constellation Based Attribute Selection Method for High Dimension...IJERA Editor
Attribute Selection is an important topic in Data Mining, because it is the effective way for reducing dimensionality, removing irrelevant data, removing redundant data, & increasing accuracy of the data. It is the process of identifying a subset of the most useful attributes that produces compatible results as the original entire set of attribute. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group called a cluster are more similar in some sense or another to each other than to those in other groups (Clusters). There are various approaches & techniques for attribute subset selection namely Wrapper approach, Filter Approach, Relief Algorithm, Distributional clustering etc. But each of one having some disadvantages like unable to handle large volumes of data, computational complexity, accuracy is not guaranteed, difficult to evaluate and redundancy detection etc. To get the upper hand on some of these issues in attribute selection this paper proposes a technique that aims to design an effective clustering based attribute selection method for high dimensional data. Initially, attributes are divided into clusters by using graph-based clustering method like minimum spanning tree (MST). In the second step, the most representative attribute that is strongly related to target classes is selected from each cluster to form a subset of attributes. The purpose is to increase the level of accuracy, reduce dimensionality; shorter training time and improves generalization by reducing over fitting.
Analysis on different Data mining Techniques and algorithms used in IOTIJERA Editor
In this paper, we discusses about five functionalities of data mining in IOT that affects the performance and that
are: Data anomaly detection, Data clustering, Data classification, feature selection, time series prediction. Some
important algorithm has also been reviewed here of each functionalities that show advantages and limitations as
well as some new algorithm that are in research direction. Here we had represent knowledge view of data
mining in IOT.
DCT AND DFT BASED BIOMETRIC RECOGNITION AND MULTIMODAL BIOMETRIC SECURITYIAEME Publication
This Research paper discusses the study and analysis conducted during this research on various techniques in biometric domain. A close glance on biometric enhancement techniques and their limitations are presented in this research paper. This process would enable researcher to understand the research contributions in the area of DCT and DFT based recognition and security, locate some crucial limitations of these notable research. This paper having summary about the different research papers that applicable to our topic of research which mentioned above. Biometric Recognition and security is a most important subject of research in this area of image processing.
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.
Hybridization of Meta-heuristics for Optimizing Routing protocol in VANETsIJERA Editor
The goal of VANET is to establish a vehicular communication system which is reliable and fast which caters to
road safety and road safety. In VANET where network fragmentation is frequent with no central control, routing
becomes a challenging task. Planning an optimal routing plan for tuning parameter configuration of routing
protocol for setting up VANET is very crucial. This is done by defining an optimization problem where
hybridization of meta-heuristics is defined. The paper contributes the idea of combining meta-heuristic
algorithm to enhance the performance of individual search method for optimization problem.
Feature Selection : A Novel Approach for the Prediction of Learning Disabilit...csandit
Feature selection is a problem closely related to dimensionality reduction. A commonly used
approach in feature selection is ranking the individual features according to some criteria and
then search for an optimal feature subset based on an evaluation criterion to test the optimality.
The objective of this work is to predict more accurately the presence of Learning Disability
(LD) in school-aged children with reduced number of symptoms. For this purpose, a novel
hybrid feature selection approach is proposed by integrating a popular Rough Set based feature
ranking process with a modified backward feature elimination algorithm. The approach follows
a ranking of the symptoms of LD according to their importance in the data domain. Each
symptoms significance or priority values reflect its relative importance to predict LD among the
various cases. Then by eliminating least significant features one by one and evaluating the
feature subset at each stage of the process, an optimal feature subset is generated. The
experimental results shows the success of the proposed method in removing redundant
attributes efficiently from the LD dataset without sacrificing the classification performance.
SURVEY ON CLASSIFICATION ALGORITHMS USING BIG DATASETEditor IJMTER
Data mining environment produces a large amount of data that need to be analyzed.
Using traditional databases and architectures, it has become difficult to process, manage and analyze
patterns. To gain knowledge about the Big Data a proper architecture should be understood.
Classification is an important data mining technique with broad applications to classify the various
kinds of data used in nearly every field of our life. Classification is used to classify the item
according to the features of the item with respect to the predefined set of classes. This paper put a
light on various classification algorithms including j48, C4.5, Naive Bayes using large dataset.
Artificial intelligence based pattern recognition is
one of the most important tools in process control to identify
process problems. The objective of this study was to
evaluate the relative performance of a feature-based
Recognizer compared with the raw data-based recognizer.
The study focused on recognition of seven commonly
researched patterns plotted on the quality chart. The
artificial intelligence based pattern recognizer trained using
the three selected statistical features resulted in significantly
better performance compared with the raw data-based
recognizer.
Semi-supervised learning approach using modified self-training algorithm to c...IJECEIAES
Burst header packet flooding is an attack on optical burst switching (OBS) network which may cause denial of service. Application of machine learning technique to detect malicious nodes in OBS network is relatively new. As finding sufficient amount of labeled data to perform supervised learning is difficult, semi-supervised method of learning (SSML) can be leveraged. In this paper, we studied the classical self-training algorithm (ST) which uses SSML paradigm. Generally, in ST, the available true-labeled data (L) is used to train a base classifier. Then it predicts the labels of unlabeled data (U). A portion from the newly labeled data is removed from U based on prediction confidence and combined with L. The resulting data is then used to re-train the classifier. This process is repeated until convergence. This paper proposes a modified self-training method (MST). We trained multiple classifiers on L in two stages and leveraged agreement among those classifiers to determine labels. The performance of MST was compared with ST on several datasets and significant improvement was found. We applied the MST on a simulated OBS network dataset and found very high accuracy with a small number of labeled data. Finally we compared this work with some related works.
Multi-Dimensional Features Reduction of Consistency Subset Evaluator on Unsup...CSCJournals
This paper presents the application of multi dimensional feature reduction of Consistency Subset Evaluator (CSE) and Principal Component Analysis (PCA) and Unsupervised Expectation Maximization (UEM) classifier for imaging surveillance system. Recently, research in image processing has raised much interest in the security surveillance systems community. Weapon detection is one of the greatest challenges facing by the community recently. In order to overcome this issue, application of the UEM classifier is performed to focus on the need of detecting dangerous weapons. However, CSE and PCA are used to explore the usefulness of each feature and reduce the multi dimensional features to simplified features with no underlying hidden structure. In this paper, we take advantage of the simplified features and classifier to categorize images object with the hope to detect dangerous weapons effectively. In order to validate the effectiveness of the UEM classifier, several classifiers are used to compare the overall accuracy of the system with the compliment from the features reduction of CSE and PCA. These unsupervised classifiers include Farthest First, Densitybased Clustering and k-Means methods. The final outcome of this research clearly indicates that UEM has the ability in improving the classification accuracy using the extracted features from the multi-dimensional feature reduction of CSE. Besides, it is also shown that PCA is able to speed-up the computational time with the reduced dimensionality of the features compromising the slight decrease of accuracy.
Minkowski Distance based Feature Selection Algorithm for Effective Intrusion ...IJMER
Intrusion Detection System (IDS) plays a major role in the provision of effective security to various types of networks. Moreover, Intrusion Detection System for networks need appropriate rule set for classifying network bench mark data into normal or attack patterns. Generally, each dataset is characterized by a large set of features. However, all these features will not be relevant or fully contribute in identifying an attack. Since different attacks need various subsets to provide better detection accuracy. In this paper an improved feature selection algorithm is proposed to identify the most appropriate subset of features for detecting a certain attacks. This proposed method is based on Minkowski distance feature ranking and an improved exhaustive search that selects a better combination of features. This system has been evaluated using the KDD CUP 1999 dataset and also with EMSVM [1] classifier. The experimental results show that the proposed system provides high classification accuracy and low false alarm rate when applied on the reduced feature subsets
A Combined Approach for Feature Subset Selection and Size Reduction for High ...IJERA Editor
selection of relevant feature from a given set of feature is one of the important issues in the field of
data mining as well as classification. In general the dataset may contain a number of features however it is not
necessary that the whole set features are important for particular analysis of decision making because the
features may share the common information‟s and can also be completely irrelevant to the undergoing
processing. This generally happen because of improper selection of features during the dataset formation or
because of improper information availability about the observed system. However in both cases the data will
contain the features that will just increase the processing burden which may ultimately cause the improper
outcome when used for analysis. Because of these reasons some kind of methods are required to detect and
remove these features hence in this paper we are presenting an efficient approach for not just removing the
unimportant features but also the size of complete dataset size. The proposed algorithm utilizes the information
theory to detect the information gain from each feature and minimum span tree to group the similar features
with that the fuzzy c-means clustering is used to remove the similar entries from the dataset. Finally the
algorithm is tested with SVM classifier using 35 publicly available real-world high-dimensional dataset and the
results shows that the presented algorithm not only reduces the feature set and data lengths but also improves the
performances of the classifier.
A Survey on Constellation Based Attribute Selection Method for High Dimension...IJERA Editor
Attribute Selection is an important topic in Data Mining, because it is the effective way for reducing dimensionality, removing irrelevant data, removing redundant data, & increasing accuracy of the data. It is the process of identifying a subset of the most useful attributes that produces compatible results as the original entire set of attribute. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group called a cluster are more similar in some sense or another to each other than to those in other groups (Clusters). There are various approaches & techniques for attribute subset selection namely Wrapper approach, Filter Approach, Relief Algorithm, Distributional clustering etc. But each of one having some disadvantages like unable to handle large volumes of data, computational complexity, accuracy is not guaranteed, difficult to evaluate and redundancy detection etc. To get the upper hand on some of these issues in attribute selection this paper proposes a technique that aims to design an effective clustering based attribute selection method for high dimensional data. Initially, attributes are divided into clusters by using graph-based clustering method like minimum spanning tree (MST). In the second step, the most representative attribute that is strongly related to target classes is selected from each cluster to form a subset of attributes. The purpose is to increase the level of accuracy, reduce dimensionality; shorter training time and improves generalization by reducing over fitting.
Analysis on different Data mining Techniques and algorithms used in IOTIJERA Editor
In this paper, we discusses about five functionalities of data mining in IOT that affects the performance and that
are: Data anomaly detection, Data clustering, Data classification, feature selection, time series prediction. Some
important algorithm has also been reviewed here of each functionalities that show advantages and limitations as
well as some new algorithm that are in research direction. Here we had represent knowledge view of data
mining in IOT.
DCT AND DFT BASED BIOMETRIC RECOGNITION AND MULTIMODAL BIOMETRIC SECURITYIAEME Publication
This Research paper discusses the study and analysis conducted during this research on various techniques in biometric domain. A close glance on biometric enhancement techniques and their limitations are presented in this research paper. This process would enable researcher to understand the research contributions in the area of DCT and DFT based recognition and security, locate some crucial limitations of these notable research. This paper having summary about the different research papers that applicable to our topic of research which mentioned above. Biometric Recognition and security is a most important subject of research in this area of image processing.
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.
Hybridization of Meta-heuristics for Optimizing Routing protocol in VANETsIJERA Editor
The goal of VANET is to establish a vehicular communication system which is reliable and fast which caters to
road safety and road safety. In VANET where network fragmentation is frequent with no central control, routing
becomes a challenging task. Planning an optimal routing plan for tuning parameter configuration of routing
protocol for setting up VANET is very crucial. This is done by defining an optimization problem where
hybridization of meta-heuristics is defined. The paper contributes the idea of combining meta-heuristic
algorithm to enhance the performance of individual search method for optimization problem.
Feature Selection : A Novel Approach for the Prediction of Learning Disabilit...csandit
Feature selection is a problem closely related to dimensionality reduction. A commonly used
approach in feature selection is ranking the individual features according to some criteria and
then search for an optimal feature subset based on an evaluation criterion to test the optimality.
The objective of this work is to predict more accurately the presence of Learning Disability
(LD) in school-aged children with reduced number of symptoms. For this purpose, a novel
hybrid feature selection approach is proposed by integrating a popular Rough Set based feature
ranking process with a modified backward feature elimination algorithm. The approach follows
a ranking of the symptoms of LD according to their importance in the data domain. Each
symptoms significance or priority values reflect its relative importance to predict LD among the
various cases. Then by eliminating least significant features one by one and evaluating the
feature subset at each stage of the process, an optimal feature subset is generated. The
experimental results shows the success of the proposed method in removing redundant
attributes efficiently from the LD dataset without sacrificing the classification performance.
SURVEY ON CLASSIFICATION ALGORITHMS USING BIG DATASETEditor IJMTER
Data mining environment produces a large amount of data that need to be analyzed.
Using traditional databases and architectures, it has become difficult to process, manage and analyze
patterns. To gain knowledge about the Big Data a proper architecture should be understood.
Classification is an important data mining technique with broad applications to classify the various
kinds of data used in nearly every field of our life. Classification is used to classify the item
according to the features of the item with respect to the predefined set of classes. This paper put a
light on various classification algorithms including j48, C4.5, Naive Bayes using large dataset.
Artificial intelligence based pattern recognition is
one of the most important tools in process control to identify
process problems. The objective of this study was to
evaluate the relative performance of a feature-based
Recognizer compared with the raw data-based recognizer.
The study focused on recognition of seven commonly
researched patterns plotted on the quality chart. The
artificial intelligence based pattern recognizer trained using
the three selected statistical features resulted in significantly
better performance compared with the raw data-based
recognizer.
Semi-supervised learning approach using modified self-training algorithm to c...IJECEIAES
Burst header packet flooding is an attack on optical burst switching (OBS) network which may cause denial of service. Application of machine learning technique to detect malicious nodes in OBS network is relatively new. As finding sufficient amount of labeled data to perform supervised learning is difficult, semi-supervised method of learning (SSML) can be leveraged. In this paper, we studied the classical self-training algorithm (ST) which uses SSML paradigm. Generally, in ST, the available true-labeled data (L) is used to train a base classifier. Then it predicts the labels of unlabeled data (U). A portion from the newly labeled data is removed from U based on prediction confidence and combined with L. The resulting data is then used to re-train the classifier. This process is repeated until convergence. This paper proposes a modified self-training method (MST). We trained multiple classifiers on L in two stages and leveraged agreement among those classifiers to determine labels. The performance of MST was compared with ST on several datasets and significant improvement was found. We applied the MST on a simulated OBS network dataset and found very high accuracy with a small number of labeled data. Finally we compared this work with some related works.
Multi-Dimensional Features Reduction of Consistency Subset Evaluator on Unsup...CSCJournals
This paper presents the application of multi dimensional feature reduction of Consistency Subset Evaluator (CSE) and Principal Component Analysis (PCA) and Unsupervised Expectation Maximization (UEM) classifier for imaging surveillance system. Recently, research in image processing has raised much interest in the security surveillance systems community. Weapon detection is one of the greatest challenges facing by the community recently. In order to overcome this issue, application of the UEM classifier is performed to focus on the need of detecting dangerous weapons. However, CSE and PCA are used to explore the usefulness of each feature and reduce the multi dimensional features to simplified features with no underlying hidden structure. In this paper, we take advantage of the simplified features and classifier to categorize images object with the hope to detect dangerous weapons effectively. In order to validate the effectiveness of the UEM classifier, several classifiers are used to compare the overall accuracy of the system with the compliment from the features reduction of CSE and PCA. These unsupervised classifiers include Farthest First, Densitybased Clustering and k-Means methods. The final outcome of this research clearly indicates that UEM has the ability in improving the classification accuracy using the extracted features from the multi-dimensional feature reduction of CSE. Besides, it is also shown that PCA is able to speed-up the computational time with the reduced dimensionality of the features compromising the slight decrease of accuracy.
Minkowski Distance based Feature Selection Algorithm for Effective Intrusion ...IJMER
Intrusion Detection System (IDS) plays a major role in the provision of effective security to various types of networks. Moreover, Intrusion Detection System for networks need appropriate rule set for classifying network bench mark data into normal or attack patterns. Generally, each dataset is characterized by a large set of features. However, all these features will not be relevant or fully contribute in identifying an attack. Since different attacks need various subsets to provide better detection accuracy. In this paper an improved feature selection algorithm is proposed to identify the most appropriate subset of features for detecting a certain attacks. This proposed method is based on Minkowski distance feature ranking and an improved exhaustive search that selects a better combination of features. This system has been evaluated using the KDD CUP 1999 dataset and also with EMSVM [1] classifier. The experimental results show that the proposed system provides high classification accuracy and low false alarm rate when applied on the reduced feature subsets
A Combined Approach for Feature Subset Selection and Size Reduction for High ...IJERA Editor
selection of relevant feature from a given set of feature is one of the important issues in the field of
data mining as well as classification. In general the dataset may contain a number of features however it is not
necessary that the whole set features are important for particular analysis of decision making because the
features may share the common information‟s and can also be completely irrelevant to the undergoing
processing. This generally happen because of improper selection of features during the dataset formation or
because of improper information availability about the observed system. However in both cases the data will
contain the features that will just increase the processing burden which may ultimately cause the improper
outcome when used for analysis. Because of these reasons some kind of methods are required to detect and
remove these features hence in this paper we are presenting an efficient approach for not just removing the
unimportant features but also the size of complete dataset size. The proposed algorithm utilizes the information
theory to detect the information gain from each feature and minimum span tree to group the similar features
with that the fuzzy c-means clustering is used to remove the similar entries from the dataset. Finally the
algorithm is tested with SVM classifier using 35 publicly available real-world high-dimensional dataset and the
results shows that the presented algorithm not only reduces the feature set and data lengths but also improves the
performances of the classifier.
JAVA 2013 IEEE PROJECT A fast clustering based feature subset selection algor...IEEEGLOBALSOFTTECHNOLOGIES
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JAVA 2013 IEEE CLOUDCOMPUTING PROJECT A fast clustering based feature subset ...IEEEGLOBALSOFTTECHNOLOGIES
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
JAVA 2013 IEEE DATAMINING PROJECT A fast clustering based feature subset sele...IEEEGLOBALSOFTTECHNOLOGIES
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The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Cloudsim a fast clustering-based feature subset selection algorithm for high...ecway
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Feature selection is a problem closely related to dimensionality reduction. A commonly used
approach in feature selection is ranking the individual features according to some criteria and
then search for an optimal feature subset based on an evaluation criterion to test the optimality.
The objective of this work is to predict more accurately the presence of Learning Disability
(LD) in school-aged children with reduced number of symptoms. For this purpose, a novel
hybrid feature selection approach is proposed by integrating a popular Rough Set based feature
ranking process with a modified backward feature elimination algorithm. The approach follows
a ranking of the symptoms of LD according to their importance in the data domain. Each
symptoms significance or priority values reflect its relative importance to predict LD among the
various cases. Then by eliminating least significant features one by one and evaluating the
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attributes efficiently from the LD dataset without sacrificing the classification performance.
A Threshold fuzzy entropy based feature selection method applied in various b...IJMER
Large amount of data have been stored and manipulated using various database
technologies. Processing all the attributes for the particular means is the difficult task. To avoid such
difficulties, feature selection process is processed.In this paper,we are collect a eight various benchmark
datasets from UCI repository.Feature selection process is carried out using fuzzy entropy based
relevance measure algorithm and follows three selection strategies like Mean selection strategy,Half
selection strategy and Neural network for threshold selection strategy. After the features are selected,
they are evaluated using Radial Basis Function (RBF) network,Stacking,Bagging,AdaBoostM1 and Antminer
classification methodologies.The test results depicts that Neural network for threshold selection
strategy works well in selecting features and Ant-miner methodology works best in bringing out better
accuracy with selected feature than processing with original dataset.The obtained result of this
experiment shows that clearly the Ant-miner is superiority than other classifiers.Thus, this proposed Antminer
algorithm could be a more suitable method for producing good results with fewer features than
the original datasets.
Automatic Feature Subset Selection using Genetic Algorithm for Clusteringidescitation
Feature subset selection is a process of selecting a
subset of minimal, relevant features and is a pre processing
technique for a wide variety of applications. High dimensional
data clustering is a challenging task in data mining. Reduced
set of features helps to make the patterns easier to understand.
Reduced set of features are more significant if they are
application specific. Almost all existing feature subset
selection algorithms are not automatic and are not application
specific. This paper made an attempt to find the feature subset
for optimal clusters while clustering. The proposed Automatic
Feature Subset Selection using Genetic Algorithm (AFSGA)
identifies the required features automatically and reduces
the computational cost in determining good clusters. The
performance of AFSGA is tested using public and synthetic
datasets with varying dimensionality. Experimental results
have shown the improved efficacy of the algorithm with optimal
clusters and computational cost.
Correlation of artificial neural network classification and nfrs attribute fi...eSAT Journals
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Keywords: ANN, SVM, PCA, CFS
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Iaetsd an efficient and large data base using subset selection algorithm
1. An Efficient and Large data base using Subset Selection Algorithm
for Multidimensional Data Extraction
M.Nagendra
M.Tech, Dept. of CSE, Audisankara College of Engineering & Technology, Gudur, A.P, India
Abstract- Feature subset selection is an effective way for reducing dimensionality, removing irrelevant
data, increasing learning accuracy and improving results comprehensibility. This process improved by
cluster based FAST Algorithm and Fuzzy Logic. FAST Algorithm can be used to Identify and removing
the irrelevant data set. This algorithm process implements using two different steps that is graph theoretic
clustering methods and representative feature cluster is selected. Feature subset selection research has
focused on searching for relevant features. The proposed fuzzy logic has focused on minimized redundant
data set and improves the feature subset accuracy.The FAST algorithm works in two steps. In the first
step, features are divided into clusters by using graph-theoretic clustering methods. In the second step, the
most representative feature that is strongly related to target classes is selected from each cluster to form a
subset of features. Features in different clusters are relatively independent; the clustering-based strategy
of FAST has a high probability of producing a subset of useful and independent features. To ensure the
efficiency of FAST, we adopt the efficient minimum-spanning tree clustering method.
Index Terms – Feature Extraction, Minimum Spanning Tree, Clustering
I. INTRODUCTION
The performance, robustness, and usefulness of classification algorithms are improved when relatively
few features are involved in the classification. Thus, selecting relevant features for the construction of
classifiers has received a great deal of attention.With the aim of choosing a subset of good features
with respect to the target concepts, feature subset selection is an effective way for reducing
dimensionality, removing irrelevant data, increasing learning accuracy, and improving result
comprehensibility. Many feature subset selection methods have been proposed and studied for
machine learning applications.
i. Existing System
The embedded methods incorporate feature selection as a part of the training process and are usually
specific to given learning algorithms, and therefore may be more efficient than the other three categories.
Traditional machine learning algorithms like decision trees or artificial neural networks are examples of
Proceedings of International Conference on Developments in Engineering Research
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2. embedded approaches. The wrapper methods use the predictive accuracy of a predetermined learning
algorithm to determine the goodness of the selected subsets, the accuracy of the learning algorithms is
usually high. However, the generality of the selected features is limited and the computational complexity
is large. The filter methods are independent of learning algorithms, with good generality. Their
computational complexity is low, but the accuracy of the learning algorithms is not guaranteed. The
hybrid methods are a combination of filter and wrapper methods by using a filter method to reduce search
space that will be considered by the subsequent wrapper. They mainly focus on combining filter and
wrapper methods to achieve the best possible performance with a particular learning algorithm with
similar time complexity of the filter methods.
ii. Proposed System
Feature subset selection can be viewed as the process of identifying and removing as many irrelevant and
redundant features as possible. This is because irrelevant features do not contribute to the predictive
accuracy and redundant features do not redound to getting a better predictor for that they provide mostly
information which is already present in other feature(s). Of the many feature subset selection algorithms,
some can effectively eliminate irrelevant features but fail to handle redundant features yet some of others
can eliminate the irrelevant while taking care of the redundant features. Our proposed FAST algorithm
falls into the second group. Traditionally, feature subset selection research has focused on searching for
relevant features. A well-known example is Relief which weighs each feature according to its ability to
discriminate instances under different targets based on distance-based criteria function. However, Relief
is ineffective at removing redundant features as two predictive but highly correlated features are likely
both to be highly weighted. Relief-F extends Relief, enabling this method to work with noisy and
incomplete data sets and to deal with multiclass problems, but still cannot identify redundant features.
iii.Framework of Feature Extraction
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3. Fig: Framework of Feature Cluster-Based Extraction Algorithm
II. FEATURE CLUSTER BASED EXTRACTION ALGORITHM
Irrelevant features, along with redundant features, severely affect the accuracy of the learning machines.
Thus, feature subset selection should be able to identify and remove as much of the irrelevant and
redundant information as possible. Moreover, “good feature subsets contain features highly correlated
with the class, yet uncorrelated with each other.” Keeping these in mind, we develop a novel algorithm
which can efficiently and effectively deal with both irrelevant and redundant features, and obtain a good
feature subset. We achieve this through a new feature selection framework which composed of the two
connected components of irrelevant feature removal and redundant feature elimination. The irrelevant
feature removal is straightforward once the right relevance measure is defined or selected, while the
redundant feature elimination is a bit of sophisticated.
In our proposed FAST algorithm, it involves (i) the construction of the minimum spanning tree (MST)
from a weighted complete graph; (ii) the partitioning of the MST into a forest with each tree representing
a cluster; and (iii) the selection of representative features from the clusters. Feature subset selection can
be the process that identifies and retains the strong T-Relevance features and selects R-Features from
feature clusters. The behind heuristics are that
1) Irrelevant features have no/weak correlation with target concept;
2) Redundant features are assembled in a cluster and a representative feature can be taken out of the
cluster.
III. ALGORITHM AND TIME COMPLEXITY ANALYSIS
The proposed FAST algorithm logically consists of three steps: (i) removing irrelevant features, (ii)
constructing a MST from relative ones, and (iii) partitioning the MST and selecting representative
features.
i. Algorithm: FAST
Inputs: ( , , … , , ) - the given data set - the − ℎ ℎ .
Output: − .
1 = 1
2 − = ( , )
3 − > ℎ
4 = ∪ { };
5 = ;
6 ℎ , ⊂
7 − = ,
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4. 8 ℎ
ℎ ℎ ℎ
9 = ( );
10 =
11 ℎ ∈
12 , < ( , ) ∧ , < , ℎ
13 = −
14 = ∅
15 ℎ ∈
16 = ∈
( , )
17 = ∪ ;
18
ii. Time Complexity
The major amount of work for Algorithm 1 involves the computation of SU values for T-
Relevance and F-Correlation, which has linear complexity in terms of the number of instances in a given
data set.
The first part of the algorithm has a linear time complexity O(m) in terms of the number of
features m. Assuming k(1≤ k ≤ m) features are selected as relevant ones in the first part, when k = 1, only
on feature is selected. The second part of the algorithm firstly constructs a complete graph from relevant
features and the complexity is O(k2
), and then generates a MST from the graph using Prim Algorithm
whose time complexity is O(k2
). The third part partitions the MST and Chooses the representative features
with the complexity of O(k). Thus when 1<k≤ m, the complexity of the Algorithm is O(m)
CONCLUSION
This project presented a novel clustering – based feature extraction algorithm for high
dimensional data. The algorithm involves 1) removing irrelevant features, 2) constructing a minimum
spanning tree from relative ones, and 3) partitioning the MST and extracting representative features.
The purpose of cluster analysis has been established to be more effective than feature extraction
algorithms. Since high dimensionality and accuracy are the two major concerns of clustering, we have
considered them together in this paper for the finer cluster for removing the irrelevant and redundant
features. The proposed supervised clustering algorithm is processed for high dimensional data to improve
the accuracy and check the probability of the patterns. Retrieval of relevant data should be faster and
more accurate. This displays results based on the high probability density thereby reducing the
dimensionality of the data.
FUTURE ENHANCEMENT
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5. In the near feature, we plan to analyze the distinct types of relationship measures and some
formal properties of feature space.
REFERENCES
[1] Almuallim H. and Dietterich T.G., Algorithms for Identifying Relevant Features, In Proceedings of
the 9th Canadian Conference on AI, pp 38-45, 1992.
[2] Arauzo-Azofra A., Benitez J.M. and Castro J.L., A feature set measure based on relief, In Proceedings
of the fifth international conference on Recent Advances in Soft Computing, pp 104-109, 2004.
[3] Bell D.A. and Wang, H., A formalism for relevance and its application in feature subset selection,
Machine Learning, 41(2), pp 175-195, 2000.
[4] Biesiada J. and Duch W., Features election for high-dimensionaldatała Pearson redundancy based
filter, AdvancesinSoftComputing, 45, pp 242C249, 2008.
[5] Butterworth R., Piatetsky-Shapiro G. and Simovici D.A., On Feature Selection through Clustering, In
Proceedings of the Fifth IEEE international Conference on Data Mining, pp 581-584, 2005.
[6] Cardie, C., Using decision trees to improve case-based learning, In Proceedings of Tenth International
Conference on Machine Learning, pp 25-32, 1993.
[7] Butterworth R., Piatetsky-Shapiro G. and Simovici D.A., On Feature Selection through Clustering, In
Proceedings of the Fifth IEEE international Conference on Data Mining, pp 581-584, 2005.
[8] Cardie, C., Using decision trees to improve case-based learning, In Proceedings of Tenth International
Conference on Machine Learning, pp 25-32, 1993.
AUTHORS
MosaNagendrahas received his B.Tech degree in Information Technology from
Jagan’s College of Engineering & Technology, Venkatachalam affiliated to JNTU,
Anantapur in 2012 and pursuing M.Tech degree in Computer Science & Engineering
at Audisankara College of Engineering & Technology, Gudur affiliated to JNTU,
Anantapur in (2012-2014).
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