Spontaneous Clustering via Minimum Gamma-divergence

Open House, ISM in Tachikawa, 2013.6.14統計数理研究所オープンハウス（立川）、H25.6.14ポスター発

Performance and Convergence Analysis of Modified C-Means Using Jeffreys-Divergence for Clustering

The size of data that we generate every day across the globe is undoubtedly astonishing due to the growth of the Internet of Things. So, it is a common practice to unravel important hidden facts and understand the massive data using clustering techniques. However, non- linear relations, which are essentially unexplored when compared to linear correlations, are more widespread within data that is high throughput. Often, nonlinear links can model a large amount of data in a more precise fashion and highlight critical trends and patterns. Moreover, selecting an appropriate measure of similarity is a well-known issue since many years when it comes to data clustering. In this work, a non-Euclidean similarity measure is proposed, which relies on non-linear Jeffreys-divergence (JS). We subsequently develop c- means using the proposed JS (J-c-means). The various properties of the JS and J-c-means are discussed. All the analyses were carried out on a few real-life and synthetic databases. The obtained outcomes show that J-c-means outperforms some cutting-edge c-means algorithms empirically

Performance and Convergence Analysis of Modified C-Means Using Jeffreys-Divergence for Clustering

This work is partially supported by the project "Prediction of diseases through computer assisted diagnosis system using images captured by minimally-invasive and non-invasive modalities", Computer Science and Engineering, PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur India (under ID: SPARC-MHRD-231). This work is also partially supported by the project Grant Agency of Excellence, Univer-sity of Hradec Kralove, Faculty of Informatics and Management, Czech Republic (under ID: UHK-FIM-GE-2204-2021).The size of data that we generate every day across the globe is undoubtedly astonishing due to the growth of the Internet of Things. So, it is a common practice to unravel important hidden facts and understand the massive data using clustering techniques. However, non- linear relations, which are essentially unexplored when compared to linear correlations, are more widespread within data that is high throughput. Often, nonlinear links can model a large amount of data in a more precise fashion and highlight critical trends and patterns. Moreover, selecting an appropriate measure of similarity is a well-known issue since many years when it comes to data clustering. In this work, a non-Euclidean similarity measure is proposed, which relies on non-linear Jeffreys-divergence (JS). We subsequently develop c- means using the proposed JS (J-c-means). The various properties of the JS and J-c-means are discussed. All the analyses were carried out on a few real-life and synthetic databases. The obtained outcomes show that J-c-means outperforms some cutting-edge c-means algorithms empirically.project "Prediction of diseases through computer assisted diagnosis system using images captured by minimally-invasive and non-invasive modalities", Computer Science and Engineering, PDPM Indian Institute of Information Technology, Design and Manufacturing SPARC-MHRD-231project Grant Agency of Excellence, University of Hradec Kralove, Faculty of Informatics and Management, Czech Republic UHK-FIM-GE-2204-202

An Enhanced Spectral Clustering Algorithm with S-Distance

This work is partially supported by the project "Prediction of diseases through computer assisted diagnosis system using images captured by minimally-invasive and non-invasive modalities", Computer Science and Engineering, PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur India (under ID: SPARCMHRD-231). This work is also partially supported by the project "Smart Solutions in Ubiquitous Computing Environments", Grant Agency of Excellence, University of Hradec Kralove, Faculty of Informatics and Management, Czech Republic (under ID: UHK-FIM-GE-2204/2021); project at Universiti Teknologi Malaysia (UTM) under Research University Grant Vot-20H04, Malaysia Research University Network (MRUN) Vot 4L876 and the Fundamental Research Grant Scheme (FRGS) Vot5F073 supported by the Ministry of Education Malaysia for the completion of the research.Calculating and monitoring customer churn metrics is important for companies to retain customers and earn more profit in business. In this study, a churn prediction framework is developed by modified spectral clustering (SC). However, the similarity measure plays an imperative role in clustering for predicting churn with better accuracy by analyzing industrial data. The linear Euclidean distance in the traditional SC is replaced by the non-linear S-distance (Sd). The Sd is deduced from the concept of S-divergence (SD). Several characteristics of Sd are discussed in this work. Assays are conducted to endorse the proposed clustering algorithm on four synthetics, eight UCI, two industrial databases and one telecommunications database related to customer churn. Three existing clustering algorithms-k-means, density-based spatial clustering of applications with noise and conventional SC-are also implemented on the above-mentioned 15 databases. The empirical outcomes show that the proposed clustering algorithm beats three existing clustering algorithms in terms of its Jaccard index, f-score, recall, precision and accuracy. Finally, we also test the significance of the clustering results by the Wilcoxon's signed-rank test, Wilcoxon's rank-sum test, and sign tests. The relative study shows that the outcomes of the proposed algorithm are interesting, especially in the case of clusters of arbitrary shape.project "Prediction of diseases through computer assisted diagnosis system using images captured by minimally-invasive and non-invasive modalities", Computer Science and Engineering, PDPM Indian Institute of Information Technology, Design and Manufacturing SPARCMHRD-231project "Smart Solutions in Ubiquitous Computing Environments", Grant Agency of Excellence, University of Hradec Kralove, Faculty of Informatics and Management, Czech Republic UHK-FIM-GE-2204/2021Universiti Teknologi Malaysia (UTM) 20H04Malaysia Research University Network (MRUN) 4L876Fundamental Research Grant Scheme (FRGS) by the Ministry of Education Malaysia 5F07