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

  1. Ayan Seal 1
  2. Aditya Karlekar 2
  3. Ondrej Krejcar 3
  4. Enrique Herrera-Viedma 4
  1. 1 PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, 482005 (India) / Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, Rokitanskeho 62, Hradec Kralove, 50003 (Czech Republic)
  2. 2 Hitkarini College of Engineering and Technology, Jabalpur, 482005 (India)
  3. 3 Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, Rokitanskeho 62, Hradec Kralove, 50003 (Czech Republic) / Malaysia Japan International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100 Kuala Lumpur (Malaysia)
  4. 4 Department Computer Science and Artificial Intelligence, University of Granada, 18071 Granada (Spain) / Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah, 21589 (Saudi Arabia)
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Revista:
IJIMAI

ISSN: 1989-1660

Año de publicación: 2021

Volumen: 7

Número: 2

Páginas: 141-149

Tipo: Artículo

DOI: 10.9781/IJIMAI.2021.04.009 DIALNET GOOGLE SCHOLAR

Otras publicaciones en: IJIMAI

Resumen

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.

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Información de financiación

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).

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