Text documents clustering using modified multi-verse optimizerIJECEIAES
In this study, a multi-verse optimizer (MVO) is utilised for the text document clus- tering (TDC) problem. TDC is treated as a discrete optimization problem, and an objective function based on the Euclidean distance is applied as similarity measure. TDC is tackled by the division of the documents into clusters; documents belonging to the same cluster are similar, whereas those belonging to different clusters are dissimilar. MVO, which is a recent metaheuristic optimization algorithm established for continuous optimization problems, can intelligently navigate different areas in the search space and search deeply in each area using a particular learning mechanism. The proposed algorithm is called MVOTDC, and it adopts the convergence behaviour of MVO operators to deal with discrete, rather than continuous, optimization problems. For evaluating MVOTDC, a comprehensive comparative study is conducted on six text document datasets with various numbers of documents and clusters. The quality of the final results is assessed using precision, recall, F-measure, entropy accuracy, and purity measures. Experimental results reveal that the proposed method performs competitively in comparison with state-of-the-art algorithms. Statistical analysis is also conducted and shows that MVOTDC can produce significant results in comparison with three well-established methods.
The analysis of proteins and messenger RNA is commonly used in the comparison of gene expression patterns in tissues or cells of different types and under distinct conditions. In gene expression analysis, normalization is a critical step as it guarantees the validity of downstream analyses. Data preprocessing is an indispensable step in the extraction and normalization of microarray gene expression data. The normalization of gene expression data is essential in ensuring accurate inferences. A number of normalization methods in high throughput sequencing studies are being employed. The preprocessing activity begins by a careful analysis of the gene expression data and usually involves the classification of many raw signal intensities into one expression value. The Robust Multiarray Average (RMA) is a normalization approach for microarrays that involves background correction, normalization and summarization of probe levels information without using MM probes (Lim et al., 2007). It is an algorithm commonly used in the creation of an expression matrix for Affymetrix data and is one of the most commonly used modes of preprocessing to normalize gene expression data. Values of raw intensity are initially background corrected and log2 transformed before being normalized. In order to generate an expression measure for probe sets on each array, a linear model is fitted to the normalized data.
Density based spatial clustering of applications with noises for dna methylat...Atef Alghzzy
Density-based spatial clustering of applications with noise (DBSCAN) is one of the most popular clustering methods to classify nonlinearly grouped data. In particular, DNA methylations are considered to be differently skewed by CpG sites and to be nonlinearly grouped by cancer statuses. Under this circumstance, DBSCAN is expected to have a desirable clustering feature. This thesis reviews the DBSCAN algorithm and compares its performance to the other traditional clustering algorithm, K-means method. Simulation studies show the misclassification ratios of DBSCAN with the comparison of K-means method to evaluate their performance, and the classification of DNA methylations from patients with lung adenocarcinoma demonstrates the application of DBSCAN.
In this video from the 2015 HPC User Forum in Broomfield, Barry Bolding from Cray presents: HPC + D + A = HPDA?
"The flexible, multi-use Cray Urika-XA extreme analytics platform addresses perhaps the most critical obstacle in data analytics today — limitation. Analytics problems are getting more varied and complex but the available solution technologies have significant constraints. Traditional analytics appliances lock you into a single approach and building a custom solution in-house is so difficult and time consuming that the business value derived from analytics fails to materialize. In contrast, the Urika-XA platform is open, high performing and cost effective, serving a wide range of analytics tools with varying computing demands in a single environment. Pre-integrated with the Hadoop and Spark frameworks, the Urika-XA system combines the benefits of a turnkey analytics appliance with a flexible, open platform that you can modify for future analytics workloads. This single-platform consolidation of workloads reduces your analytics footprint and total cost of ownership."
Learn more: http://www.cray.com/products/analytics/urika-xa
Watch the video presentation: http://wp.me/p3RLEV-3yR
Sign up for our insideBIGDATA Newsletter: http://insidebigdata.com/newsletter
An Efficient Clustering Method for Aggregation on Data FragmentsIJMER
Clustering is an important step in the process of data analysis with applications to numerous fields. Clustering ensembles, has emerged as a powerful technique for combining different clustering results to obtain a quality cluster. Existing clustering aggregation algorithms are applied directly to large number of data points. The algorithms are inefficient if the number of data points is large. This project defines an efficient approach for clustering aggregation based on data fragments. In fragment-based approach, a data fragment is any subset of the data. To increase the efficiency of the proposed approach, the clustering aggregation can be performed directly on data fragments under comparison measure and normalized mutual information measures for clustering aggregation, enhanced clustering aggregation algorithms are described. To show the minimal computational complexity. (Agglomerative, Furthest, and Local Search); nevertheless, which increases the accuracy.
WITH SEMANTICS AND HIDDEN MARKOV MODELS TO AN ADAPTIVE LOG FILE PARSERijnlc
We aim to model an adaptive log file parser. As the content of log files often evolves over time, we established a dynamic statistical model which learns and adapts processing and parsing rules. First, we limit the amount of unstructured text by clustering based on semantics of log file lines. Next, we only take the most relevant cluster into account and focus only on those frequent patterns which lead to the desired output table similar to Vaarandi [10]. Furthermore, we transform the found frequent patterns and the output stating the parsed table into a Hidden Markov Model (HMM). We use this HMM as a specific, however, flexible representation of a pattern for log file parsing to maintain high quality output. After training our model on one system type and applying it to a different system with slightly different log file patterns, we achieve an accuracy over 99.99%
Text documents clustering using modified multi-verse optimizerIJECEIAES
In this study, a multi-verse optimizer (MVO) is utilised for the text document clus- tering (TDC) problem. TDC is treated as a discrete optimization problem, and an objective function based on the Euclidean distance is applied as similarity measure. TDC is tackled by the division of the documents into clusters; documents belonging to the same cluster are similar, whereas those belonging to different clusters are dissimilar. MVO, which is a recent metaheuristic optimization algorithm established for continuous optimization problems, can intelligently navigate different areas in the search space and search deeply in each area using a particular learning mechanism. The proposed algorithm is called MVOTDC, and it adopts the convergence behaviour of MVO operators to deal with discrete, rather than continuous, optimization problems. For evaluating MVOTDC, a comprehensive comparative study is conducted on six text document datasets with various numbers of documents and clusters. The quality of the final results is assessed using precision, recall, F-measure, entropy accuracy, and purity measures. Experimental results reveal that the proposed method performs competitively in comparison with state-of-the-art algorithms. Statistical analysis is also conducted and shows that MVOTDC can produce significant results in comparison with three well-established methods.
The analysis of proteins and messenger RNA is commonly used in the comparison of gene expression patterns in tissues or cells of different types and under distinct conditions. In gene expression analysis, normalization is a critical step as it guarantees the validity of downstream analyses. Data preprocessing is an indispensable step in the extraction and normalization of microarray gene expression data. The normalization of gene expression data is essential in ensuring accurate inferences. A number of normalization methods in high throughput sequencing studies are being employed. The preprocessing activity begins by a careful analysis of the gene expression data and usually involves the classification of many raw signal intensities into one expression value. The Robust Multiarray Average (RMA) is a normalization approach for microarrays that involves background correction, normalization and summarization of probe levels information without using MM probes (Lim et al., 2007). It is an algorithm commonly used in the creation of an expression matrix for Affymetrix data and is one of the most commonly used modes of preprocessing to normalize gene expression data. Values of raw intensity are initially background corrected and log2 transformed before being normalized. In order to generate an expression measure for probe sets on each array, a linear model is fitted to the normalized data.
Density based spatial clustering of applications with noises for dna methylat...Atef Alghzzy
Density-based spatial clustering of applications with noise (DBSCAN) is one of the most popular clustering methods to classify nonlinearly grouped data. In particular, DNA methylations are considered to be differently skewed by CpG sites and to be nonlinearly grouped by cancer statuses. Under this circumstance, DBSCAN is expected to have a desirable clustering feature. This thesis reviews the DBSCAN algorithm and compares its performance to the other traditional clustering algorithm, K-means method. Simulation studies show the misclassification ratios of DBSCAN with the comparison of K-means method to evaluate their performance, and the classification of DNA methylations from patients with lung adenocarcinoma demonstrates the application of DBSCAN.
In this video from the 2015 HPC User Forum in Broomfield, Barry Bolding from Cray presents: HPC + D + A = HPDA?
"The flexible, multi-use Cray Urika-XA extreme analytics platform addresses perhaps the most critical obstacle in data analytics today — limitation. Analytics problems are getting more varied and complex but the available solution technologies have significant constraints. Traditional analytics appliances lock you into a single approach and building a custom solution in-house is so difficult and time consuming that the business value derived from analytics fails to materialize. In contrast, the Urika-XA platform is open, high performing and cost effective, serving a wide range of analytics tools with varying computing demands in a single environment. Pre-integrated with the Hadoop and Spark frameworks, the Urika-XA system combines the benefits of a turnkey analytics appliance with a flexible, open platform that you can modify for future analytics workloads. This single-platform consolidation of workloads reduces your analytics footprint and total cost of ownership."
Learn more: http://www.cray.com/products/analytics/urika-xa
Watch the video presentation: http://wp.me/p3RLEV-3yR
Sign up for our insideBIGDATA Newsletter: http://insidebigdata.com/newsletter
An Efficient Clustering Method for Aggregation on Data FragmentsIJMER
Clustering is an important step in the process of data analysis with applications to numerous fields. Clustering ensembles, has emerged as a powerful technique for combining different clustering results to obtain a quality cluster. Existing clustering aggregation algorithms are applied directly to large number of data points. The algorithms are inefficient if the number of data points is large. This project defines an efficient approach for clustering aggregation based on data fragments. In fragment-based approach, a data fragment is any subset of the data. To increase the efficiency of the proposed approach, the clustering aggregation can be performed directly on data fragments under comparison measure and normalized mutual information measures for clustering aggregation, enhanced clustering aggregation algorithms are described. To show the minimal computational complexity. (Agglomerative, Furthest, and Local Search); nevertheless, which increases the accuracy.
WITH SEMANTICS AND HIDDEN MARKOV MODELS TO AN ADAPTIVE LOG FILE PARSERijnlc
We aim to model an adaptive log file parser. As the content of log files often evolves over time, we established a dynamic statistical model which learns and adapts processing and parsing rules. First, we limit the amount of unstructured text by clustering based on semantics of log file lines. Next, we only take the most relevant cluster into account and focus only on those frequent patterns which lead to the desired output table similar to Vaarandi [10]. Furthermore, we transform the found frequent patterns and the output stating the parsed table into a Hidden Markov Model (HMM). We use this HMM as a specific, however, flexible representation of a pattern for log file parsing to maintain high quality output. After training our model on one system type and applying it to a different system with slightly different log file patterns, we achieve an accuracy over 99.99%
A SURVEY ON OPTIMIZATION APPROACHES TO TEXT DOCUMENT CLUSTERINGijcsa
Text Document Clustering is one of the fastest growing research areas because of availability of huge amount of information in an electronic form. There are several number of techniques launched for clustering documents in such a way that documents within a cluster have high intra-similarity and low inter-similarity to other clusters. Many document clustering algorithms provide localized search in effectively navigating, summarizing, and organizing information. A global optimal solution can be obtained by applying high-speed and high-quality optimization algorithms. The optimization technique performs a globalized search in the entire solution space. In this paper, a brief survey on optimization approaches to text document clustering is turned out.
Improved fuzzy c-means algorithm based on a novel mechanism for the formation...TELKOMNIKA JOURNAL
The clustering approach is considered as a vital method for many fields suchas machine learning, pattern recognition, image processing, information retrieval, data compression, computer graphics, and others.Similarly, it hasgreat significance in wireless sensor networks (WSNs) by organizing thesensor nodes into specific clusters. Consequently, saving energy and prolonging network lifetime, which is totally dependent on the sensor’s battery, that is considered asa major challenge in the WSNs. Fuzzyc-means (FCM) is one of classification algorithm, which is widely used in literature for this purpose in WSNs. However, according to the nature of random nodes deployment manner, on certain occasions, this situation forces this algorithm to produce unbalanced clusters, which adversely affects the lifetime of the network.To overcome this problem, a new clustering method called FCM-CMhas been proposed by improving the FCM algorithm to form balanced clustersfor random nodes deployment. The improvement is conductedby integrating the FCM with a centralized mechanism(CM).The proposed method will be evaluated based on four new parameters. Simulation result shows that our proposed algorithm is more superior to FCM by producing balanced clustersin addition to increasing the balancing of the intra-distances of the clusters, which leads to energy conservation and prolonging network lifespan.
Particle Swarm Optimization based K-Prototype Clustering Algorithm iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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.
Clustering is also known as data segmentation aims to partitions data set into groups, clusters, according to their similarity. Cluster analysis has been extensively studied in many researches. There are many algorithms for different types of clustering. These classical algorithms can't be applied on big data due to its distinct features. It is a challenge to apply the traditional techniques on large unstructured data. This study proposes a hybrid model to cluster big data using the famous traditional K-means clustering algorithm. The proposed model consists of three phases namely; Mapper phase, Clustering Phase and Reduce phase. The first phase uses map-reduce algorithm to split big data into small datasets. Whereas, the second phase implements the traditional clustering K-means algorithm on each of the spitted small data sets. The last phase is responsible of producing the general clusters output of the complete data set. Two functions, Mode and Fuzzy Gaussian, have been implemented and compared at the last phase to determine the most suitable one. The experimental study used four benchmark big data sets; Covtype, Covtype-2, Poker, and Poker-2. The results proved the efficiency of the proposed model in clustering big data using the traditional K-means algorithm. Also, the experiments show that the Fuzzy Gaussian function produces more accurate results than the traditional Mode function.
A Novel Multi- Viewpoint based Similarity Measure for Document ClusteringIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
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.
Information extraction from data is one of the key necessities for data analysis. Unsupervised nature of data leads to complex computational methods for analysis. This paper presents a density based spatial clustering technique integrated with one-class Support Vector Machine (SVM), a machine learning technique for noise reduction, a modified variant of DBSCAN called Noise Reduced DBSCAN (NRDBSCAN). Analysis of DBSCAN exhibits its major requirement of accurate thresholds, absence of which yields suboptimal results. However, identifying accurate threshold settings is unattainable. Noise is one of the major side-effects of the threshold gap. The proposed work reduces noise by integrating a machine learning classifier into the operation structure of DBSCAN. The Experimental results indicate high homogeneity levels in the clustering process.
Further Analysis Of A Framework To Analyze Network Performance Based On Infor...CSCJournals
Abstract In [1], Geng and Li presented a framework to analyze network performance based on information quality. In that paper, the authors based their framework on the flow of information from a Base Station (BS) to clients. The theory they established can, and needs, to be extended to accommodate for the flow of information from the clients to the BS. In this work, we use that framework and study the case of client to BS data transmission. Our work closely parallels the work of Geng and Li, we use the same notation and liberally reference their work. Keywords: information theory, information quality, network protocols, network performance
Data reduction techniques for high dimensional biological dataeSAT Journals
Abstract
High dimensional biological datasets in recent years has been growing rapidly. Extracting the knowledge and analyzing highdimensional
biological data is one the key challenges in which variety and veracity are the two distinct characteristics. The
question that arises now is, how to perform dimensionality reduction for this heterogeneous data and how to develop a high
performance platform to efficiently analyze high dimensional biological data and how to find the useful things from this data. To
deeply discuss this issue, this paper begins with a brief introduction to data analytics available for biological data, followed by
the discussions of big data analytics and then a survey on various data reduction methods for biological data. We propose a dense
clustering algorithm for standard high dimensional biological data.
Keywords: Big Data Analytics, Dimensionality Reduction
Large scale cell tracking using an approximated Sinkhorn algorithmParth Nandedkar
Cell tracking for a large scale (of over 1 million cells) has not yet been achievable within reasonable a time scope with current NN/RNN/Bi-RNN based methods. This individual research conducted by me at Osaka University, ISIR seeks to solve this problem using the Sinkhorn algorithm, and taking inspiration from the MPM method (Hayashida, 2020)
Data mining is utilized to manage huge measure of information which are put in the data ware houses and databases, to discover required information and data. Numerous data mining systems have been proposed, for example, association rules, decision trees, neural systems, clustering, and so on. It has turned into the purpose of consideration from numerous years. A re-known amongst the available data mining strategies is clustering of the dataset. It is the most effective data mining method. It groups the dataset in number of clusters based on certain guidelines that are predefined. It is dependable to discover the connection between the distinctive characteristics of data.
In k-mean clustering algorithm, the function is being selected on the basis of the relevancy of the function for predicting the data and also the Euclidian distance between the centroid of any cluster and the data objects outside the cluster is being computed for the clustering the data points. In this work, author enhanced the Euclidian distance formula to increase the cluster quality.
The problem of accuracy and redundancy of the dissimilar points in the clusters remains in the improved k-means for which new enhanced approach is been proposed which uses the similarity function for checking the similarity level of the point before including it to the cluster.
Heterogeneous Information Network Embedding for Recommendation
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.org
Heterogeneous Information Network Embedding for Recommendation
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.org
A SURVEY ON OPTIMIZATION APPROACHES TO TEXT DOCUMENT CLUSTERINGijcsa
Text Document Clustering is one of the fastest growing research areas because of availability of huge amount of information in an electronic form. There are several number of techniques launched for clustering documents in such a way that documents within a cluster have high intra-similarity and low inter-similarity to other clusters. Many document clustering algorithms provide localized search in effectively navigating, summarizing, and organizing information. A global optimal solution can be obtained by applying high-speed and high-quality optimization algorithms. The optimization technique performs a globalized search in the entire solution space. In this paper, a brief survey on optimization approaches to text document clustering is turned out.
Improved fuzzy c-means algorithm based on a novel mechanism for the formation...TELKOMNIKA JOURNAL
The clustering approach is considered as a vital method for many fields suchas machine learning, pattern recognition, image processing, information retrieval, data compression, computer graphics, and others.Similarly, it hasgreat significance in wireless sensor networks (WSNs) by organizing thesensor nodes into specific clusters. Consequently, saving energy and prolonging network lifetime, which is totally dependent on the sensor’s battery, that is considered asa major challenge in the WSNs. Fuzzyc-means (FCM) is one of classification algorithm, which is widely used in literature for this purpose in WSNs. However, according to the nature of random nodes deployment manner, on certain occasions, this situation forces this algorithm to produce unbalanced clusters, which adversely affects the lifetime of the network.To overcome this problem, a new clustering method called FCM-CMhas been proposed by improving the FCM algorithm to form balanced clustersfor random nodes deployment. The improvement is conductedby integrating the FCM with a centralized mechanism(CM).The proposed method will be evaluated based on four new parameters. Simulation result shows that our proposed algorithm is more superior to FCM by producing balanced clustersin addition to increasing the balancing of the intra-distances of the clusters, which leads to energy conservation and prolonging network lifespan.
Particle Swarm Optimization based K-Prototype Clustering Algorithm iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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.
Clustering is also known as data segmentation aims to partitions data set into groups, clusters, according to their similarity. Cluster analysis has been extensively studied in many researches. There are many algorithms for different types of clustering. These classical algorithms can't be applied on big data due to its distinct features. It is a challenge to apply the traditional techniques on large unstructured data. This study proposes a hybrid model to cluster big data using the famous traditional K-means clustering algorithm. The proposed model consists of three phases namely; Mapper phase, Clustering Phase and Reduce phase. The first phase uses map-reduce algorithm to split big data into small datasets. Whereas, the second phase implements the traditional clustering K-means algorithm on each of the spitted small data sets. The last phase is responsible of producing the general clusters output of the complete data set. Two functions, Mode and Fuzzy Gaussian, have been implemented and compared at the last phase to determine the most suitable one. The experimental study used four benchmark big data sets; Covtype, Covtype-2, Poker, and Poker-2. The results proved the efficiency of the proposed model in clustering big data using the traditional K-means algorithm. Also, the experiments show that the Fuzzy Gaussian function produces more accurate results than the traditional Mode function.
A Novel Multi- Viewpoint based Similarity Measure for Document ClusteringIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
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.
Information extraction from data is one of the key necessities for data analysis. Unsupervised nature of data leads to complex computational methods for analysis. This paper presents a density based spatial clustering technique integrated with one-class Support Vector Machine (SVM), a machine learning technique for noise reduction, a modified variant of DBSCAN called Noise Reduced DBSCAN (NRDBSCAN). Analysis of DBSCAN exhibits its major requirement of accurate thresholds, absence of which yields suboptimal results. However, identifying accurate threshold settings is unattainable. Noise is one of the major side-effects of the threshold gap. The proposed work reduces noise by integrating a machine learning classifier into the operation structure of DBSCAN. The Experimental results indicate high homogeneity levels in the clustering process.
Further Analysis Of A Framework To Analyze Network Performance Based On Infor...CSCJournals
Abstract In [1], Geng and Li presented a framework to analyze network performance based on information quality. In that paper, the authors based their framework on the flow of information from a Base Station (BS) to clients. The theory they established can, and needs, to be extended to accommodate for the flow of information from the clients to the BS. In this work, we use that framework and study the case of client to BS data transmission. Our work closely parallels the work of Geng and Li, we use the same notation and liberally reference their work. Keywords: information theory, information quality, network protocols, network performance
Data reduction techniques for high dimensional biological dataeSAT Journals
Abstract
High dimensional biological datasets in recent years has been growing rapidly. Extracting the knowledge and analyzing highdimensional
biological data is one the key challenges in which variety and veracity are the two distinct characteristics. The
question that arises now is, how to perform dimensionality reduction for this heterogeneous data and how to develop a high
performance platform to efficiently analyze high dimensional biological data and how to find the useful things from this data. To
deeply discuss this issue, this paper begins with a brief introduction to data analytics available for biological data, followed by
the discussions of big data analytics and then a survey on various data reduction methods for biological data. We propose a dense
clustering algorithm for standard high dimensional biological data.
Keywords: Big Data Analytics, Dimensionality Reduction
Large scale cell tracking using an approximated Sinkhorn algorithmParth Nandedkar
Cell tracking for a large scale (of over 1 million cells) has not yet been achievable within reasonable a time scope with current NN/RNN/Bi-RNN based methods. This individual research conducted by me at Osaka University, ISIR seeks to solve this problem using the Sinkhorn algorithm, and taking inspiration from the MPM method (Hayashida, 2020)
Data mining is utilized to manage huge measure of information which are put in the data ware houses and databases, to discover required information and data. Numerous data mining systems have been proposed, for example, association rules, decision trees, neural systems, clustering, and so on. It has turned into the purpose of consideration from numerous years. A re-known amongst the available data mining strategies is clustering of the dataset. It is the most effective data mining method. It groups the dataset in number of clusters based on certain guidelines that are predefined. It is dependable to discover the connection between the distinctive characteristics of data.
In k-mean clustering algorithm, the function is being selected on the basis of the relevancy of the function for predicting the data and also the Euclidian distance between the centroid of any cluster and the data objects outside the cluster is being computed for the clustering the data points. In this work, author enhanced the Euclidian distance formula to increase the cluster quality.
The problem of accuracy and redundancy of the dissimilar points in the clusters remains in the improved k-means for which new enhanced approach is been proposed which uses the similarity function for checking the similarity level of the point before including it to the cluster.
Heterogeneous Information Network Embedding for Recommendation
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.org
Heterogeneous Information Network Embedding for Recommendation
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.org
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/11/learning-compact-dnn-models-for-embedded-vision-a-presentation-from-the-university-of-maryland-at-college-park/
Shuvra Bhattacharyya, Professor at the University of Maryland at College Park, presents the “Learning Compact DNN Models for Embedded Vision” tutorial at the May 2023 Embedded Vision Summit.
In this talk, Bhattacharyya explores methods to transform large deep neural network (DNN) models into effective compact models. The transformation process that he focuses on—from large to compact DNN form—is referred to as pruning. Pruning involves the removal of neurons or parameters from a neural network. When performed strategically, pruning can lead to significant reductions in computational complexity without significant degradation in accuracy. It is sometimes even possible to increase accuracy through pruning.
Pruning provides a general approach for facilitating real-time inference in resource-constrained embedded computer vision systems. Bhattacharyya provides an overview of important aspects to consider when applying or developing a DNN pruning method and presents details on a recently introduced pruning method called NeuroGRS. NeuroGRS considers structures and trained weights jointly throughout the pruning process and can result in significantly more compact models compared to other pruning methods.
A Threshold Fuzzy Entropy Based Feature Selection: Comparative StudyIJMER
Feature selection is one of the most common and critical tasks in database classification. It
reduces the computational cost by removing insignificant and unwanted features. Consequently, this
makes the diagnosis process accurate and comprehensible. This paper presents the measurement of
feature relevance based on fuzzy entropy, tested with Radial Basis Classifier (RBF) network,
Bagging(Bootstrap Aggregating), Boosting and stacking for various fields of datasets. Twenty
benchmarked datasets which are available in UCI Machine Learning Repository and KDD have been
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6. Single omic study
● One-dimension data explains the
diagnostics and progression for
complex disorders
● Information is limited
● Different layers of biological
system are relevant and
dependent
6
7. Omic data integration objectives
● Promoting precise medicine from big data
● Multiview investigation on the
completeness and complexity of the
biological system
● Discover hidden biological regularities
● Make use of complementary information
and discover biomarkers for diagnosis,
progression and treatment in human
diseases
7
8. Data Integration Challenges (From Computational)
● Data integration is broad
● Data heterogeneity
● Data unification
● Data noise and bias
● Data integration and dimensionality reduction
8
10. Unsupervised classification
● Matrix factorization methods (iCluster and iCluster+ )
○ Assumption: common latent variable in different data
● Bayesian methods (Bayesian consensus clustering)
○ Assumption: assumptions on data distribution and data correlation
● Network-based methods (SNF)
○ Assumption: samples relationship can be enhanced from
complementary multiple omic data
● Multiple Kernel Learning and Multi-Step Analysis (rMKL-LPP)
○ Assumption: pattern in a lower dimensional and integrative
subspace
10
11. Data Integration for subtype discovery
● Data Source
○ Gene expression; DNA methylation; gene mutation
● Procedures
○ Data fusion -- Clustering -- Evaluation
● Biological interpretation
○ Molecular alterations
○ Survival outcome
○ Response to therapies
11
14. Procedure
● Data Fusion and K-means model selection
○ EM algorithm to obtain maximum
likelihood estimates
■ E-step provides a simultaneous
dimension reduction
■ M-step is to update the parameter
estimates
● Evaluation
○ Proportion of deviance -- POD (d/n^2)
○ Smaller, stronger cluster separability
○ Determine cluster number and lasso
parameter λ 15
16. Summaries
● The joint latent variable model is completely scalable to include additional
data types
● iCluster have been applied to discover subtypes at breast cancer and
glioblastoma multiforme (GBM)
● iCluster+ makes different modeling assumptions on data types: binary,
continuous, categorical, and sequential data
17
18. SNF data fusion
1. Calculate sample similarity W in each omic dataset
using (1)
2. Calculate normalized weight matrix P from W using (2)
3. Use K nearest neighbors (KNN) to calculate local
affinity matrix S through the formulas (3) from W. P
carries the full information about the similarity of each
patient to all others whereas S only encodes the
similarity to the K most similar patients for each
patient.
4. Network fusion process: for 2 datasets, P1, S1 and P2,
S2 can be calculated, then iteratively update P1 and P2
for t steps using (4) and (5); for more than 2 datasets,
update the Ps using (5)
5. Obtain the overall fused matrix P by averaging the
updated single Ps
19
19. Spectral Clustering
Input X (n x n sample similarity matrix) and k clusters
Goal subgroups in a graph with disjoint cliques
Procedures:
1. Compute the normalized Laplacian L
2. Compute the first eigenvectors u and eigenvalues
for L
3. Let U be the matrix containing eigenvectors u as
columns
4. Form the matrix T from U by normalizing the rows
to norm 1
5. Cluster the points with k-means into clusters C1, ...,
Ck
20
20. Application: GBM subtype discovery
Evaluations:
1. P value in Cox log-rank test
2. Silhouette score
21
21. Summaries
● SNF can construct sample sample network by integrating multiple datasets
● SNF can be expanded to include more datasets and be applied in more
questions
22
22. Bayesian Consensus Clustering
● An integrative statistical model that permits a separate clustering of the
objects for each data source.
● These separate clusterings adhere loosely to an overall consensus clustering
● BCC do simultaneous estimation of both the consensus clustering and the
source-specific clusterings
23
23. Procedures
● Dirichlet mixture model to accommodate multiple data (X)
● Probability of belonging to one cluster
● Estimation
○ Gibbs sampling procedure to approximate the posterior distribution
○ Markov chain Monte Carlo (MCMC) proceeds by iteratively sampling
● Choose K based on highest mean adjusted adherence
24
24. Application on breast cancer
● RNA gene expression (GE) data
for 645 genes.
● DNA methylation (ME) data for
574 probes.
● miRNA expression (miRNA) data
for 423 miRNAs.
● Reverse phase protein array
(RPPA) data for 171 proteins.
25
26. Summaries
1. BCC model assumes a simple and general dependence between data
sources.
2. BCC models both an overall clustering and a clustering specific to each data
source, with advantages over traditional methods in terms of modeling
uncertainty and the ability to borrow information across sources.
3. BCC is suitable to work on multisource biomedical data, as well may be used
to compare clusterings from different statistical models for a single
homogeneous dataset.
27
27. Regularized Multiple Kernel Learning Locality
Preserving Projections (rMKL-LPP)
28
● It is an extension of the current multiple kernel learning with dimensional
reduction (MKL-DR) method, where the data are projected into a lower
dimensional and integrative subspace.
● A regularization term is added to avoid overfitting during the optimization
procedure, and it allows using several different kernel types.
● The Locality Preserving Projections (LPP) is applied to conserve the
sum of distances for each sample’s k-Nearest Neighbors.
28. Procedures
● Data fusion
○ rMKL-LPP
○ Optimization
○ integrated kernel matrix
● Clustering
○ K-means
○ Mean silhouette width used to optimize number of clusters
● Evaluation
○ Silhouette score and cross validation (Rand index)
29
29. Applications in 5 cancers
1. Comparison to state-of-the-art (SNF)
2. Robustness analysis
3. Comparison of clusterings to
established subtypes
4. Clinical implications from clusterings
30
5 cancers
1. glioblastoma multiforme (GBM) --
213 samples
2. breast invasive carcinoma (BIC) --
105 samples
3. kidney renal clear cell carcinoma
(KRCCC) -- 122 samples
4. lung squamous cell carcinoma
(LSCC) -- 106 samples
5. colon adenocarcinoma (COAD) -- 92
samplesDatasets: gene expression, DNA methylation
and miRNA expression data
31. 2. Robustness analysis
32
Fig. 2. Robustness of clustering for leave-one-out
datasets measured using Rand index.
Fig. 3. Robustness of clustering for leave-
one-out cross-validation applied to
reduced sized datasets measured using
Rand index.
35. Summaries
1. rMKL-LPP found subtypes with more interesting log-rank test compared to the
state-of-the-art method
2. Several kernel matrices per data type can improve performance burdance,
remove the burden of selecting the optimal kernel matrix and have fair
stability
3. rMKL-LPP compared to unregularized MKL-DR remains stable also for small
datasets
4. The application at GBM shows to capture this diverse information within one
clustering
36
36. References
1. Huang, S., Chaudhary, K. & Garmire, L. X. More Is Better: Recent Progress in Multi-Omics Data
Integration Methods. Front. Genet. 8, 84 (2017).
2. Wang, B. et al. Similarity network fusion for aggregating data types on a genomic scale. Nat.
Methods 11, 333–337 (2014).
3. Shen, R., Olshen, A. B. & Ladanyi, M. Integrative clustering of multiple genomic data types using a
joint latent variable model with application to breast and lung cancer subtype analysis.
Bioinformatics 25, 2906–2912 (2009).
4. Shen, R. et al. Integrative subtype discovery in glioblastoma using iCluster. PLoS One 7, e35236
(2012).
5. Mo, Q. et al. Pattern discovery and cancer gene identification in integrated cancer genomic data.
Proc. Natl. Acad. Sci. U. S. A. 110, 4245–4250 (2013).
6. Speicher, N. K. & Pfeifer, N. Integrating different data types by regularized unsupervised multiple
kernel learning with application to cancer subtype discovery. Bioinformatics 31, i268–75 (2015).
7. Lock, E. F. & Dunson, D. B. Bayesian consensus clustering. Bioinformatics 29, 2610–2616 (2013).
37
Editor's Notes
The main advantage of Bayesian methods in data integration is that they can make assumptions not only on different types of data sets with various distributions but also on the correlations among data sets.
estimating the number of clusters K and the lasso parameter λ.
(C) Model selection based on POD measure. A four-cluster sparse solution (λ = 0.2) was chosen.
Spectral clustering is suitable for graph clustering
It is an extension of the current multiple kernel learning with dimensional reduction (MKL-DR) method
MKL-DR: https://pdfs.semanticscholar.org/1cd3/bbae54b217843870fdc771d727b6043225b8.pdf
Fig. 2. Robustness of clustering for leave-one-out datasets measured using Rand index. Each patient is left out once in the dimensionality reduction and clustering procedure and afterwards added to the cluster with the closest mean based on the learned projection for this data point, which is given by projðxiÞ ¼ AT Ki b. The resulting cluster assignment is then compared with the clustering of the whole dataset. The error bars represent one standard deviation
Fig. 3. Robustness of clustering for leave-one-out cross-validation applied to reduced sized datasets measured using Rand index. For each cancer type, we sampled 20 times half of the patients and applied leave-one-out cross-validation as described in Section 3.4. The error bars represent one standard deviation
The results are very similar to those found by Noushmehr et al. (2010) for their identified G-CIMP positive subtype. In addition, we found the set of underexpressed genes to be highly enriched for processes associated to the immune system and inflammation [cf. Table 3 (column 2)]. Since chronic inflammation is generally related to cancer progression and is thought to play an important role in the construction of the tumor microenvironment (Hanahan and Weinberg, 2011), these downregulations might be a reason for the favorable outcome of patients from this cluster.