Open AccessJournal Article
A New Cluster Validity Index for Fuzzy Clustering
TLDR
A new validity index for determining the number of clusters is proposed, based on a novel way of combining cohesion and discrepancy, which shows clearly the efficiency of the new index under the condition of overlapping clusters.Abstract:
In this paper, we propose a new validity index for determining the number of clusters. It is based on a novel way of combining cohesion and discrepancy. Extensive tests of the index in a conventional model selection process (FCM algorithm) have been performed using generated data sets and public domain data sets,and comparison with several existing and important indices has been made. The results obtained show clearly the efficiency of the new index under the condition of overlapping clusters.read more
Citations
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Journal ArticleDOI
Clustering Algorithms: Their Application to Gene Expression Data
Jelili Oyelade,Itunuoluwa Isewon,Funke Oladipupo,Olufemi Aromolaran,Efosa Uwoghiren,Faridah Ameh,Moses Achas,Ezekiel Adebiyi +7 more
TL;DR: This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure.
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A Survey on Internal Validity Measure for Cluster Validation
TL;DR: A detailed description of the mathematical working of few cluster validity indices and not all is presented, to classify these indices and to explore the ideas for the future promotion of the work in the domain of cluster validation.
A Hierarchical Method for Determining the Number of Clusters
TL;DR: A hierarchical method is proposed to get rid of repeatedly clustering on large datasets, which outperforms the recently published approaches, while the efficiency is significantly improved.
Journal ArticleDOI
Multi-Objective Optimized Fuzzy Clustering for Detecting Cell Clusters from Single-Cell Expression Profiles
Saurav Mallik,Zhongming Zhao +1 more
TL;DR: A multi-objective optimization-based fuzzy clustering approach for detecting cell clusters from scRNA-seq data that obtained differentially expressed genes (DEGs) using Limma through the comparison of expression of the samples between each resultant cluster and the remaining clusters.
Proceedings ArticleDOI
Measuring Overlap-Rate in Hierarchical Cluster Merging for Image Segmentation and Ship Detection
TL;DR: A definition on the degree of overlap between two clusters and an algorithm for calculating the overlap rate are presented and a new hierarchical cluster merging algorithm for image segmentation is developed and applied to the ship detection in high resolution image.
References
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Journal ArticleDOI
Clustering Algorithms: Their Application to Gene Expression Data
Jelili Oyelade,Itunuoluwa Isewon,Funke Oladipupo,Olufemi Aromolaran,Efosa Uwoghiren,Faridah Ameh,Moses Achas,Ezekiel Adebiyi +7 more
TL;DR: This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure.
Journal ArticleDOI
A Survey on Internal Validity Measure for Cluster Validation
TL;DR: A detailed description of the mathematical working of few cluster validity indices and not all is presented, to classify these indices and to explore the ideas for the future promotion of the work in the domain of cluster validation.
A Hierarchical Method for Determining the Number of Clusters
TL;DR: A hierarchical method is proposed to get rid of repeatedly clustering on large datasets, which outperforms the recently published approaches, while the efficiency is significantly improved.
Journal ArticleDOI
Multi-Objective Optimized Fuzzy Clustering for Detecting Cell Clusters from Single-Cell Expression Profiles
Saurav Mallik,Zhongming Zhao +1 more
TL;DR: A multi-objective optimization-based fuzzy clustering approach for detecting cell clusters from scRNA-seq data that obtained differentially expressed genes (DEGs) using Limma through the comparison of expression of the samples between each resultant cluster and the remaining clusters.
Proceedings ArticleDOI
Measuring Overlap-Rate in Hierarchical Cluster Merging for Image Segmentation and Ship Detection
TL;DR: A definition on the degree of overlap between two clusters and an algorithm for calculating the overlap rate are presented and a new hierarchical cluster merging algorithm for image segmentation is developed and applied to the ship detection in high resolution image.