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

• Ayan Seal ^[1] ; Aditya Karlekar ^[3] ; Ondrej Krejcar ^[4] ; Enrique Herrera-Viedma ^[2]
  1. [1] Indian Institute of Information Technology

     Indian Institute of Information Technology

     India

     
  2. [2] Universidad de Granada

     Universidad de Granada

     Granada, España

     
  3. [3] Hitkarini College of Engineering and Technology, Jabalpur, 482005 (India)
  4. [4] 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)

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• Localización: IJIMAI, ISSN-e 1989-1660, Vol. 7, Nº. 2, 2021, págs. 141-149
• Idioma: inglés
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• Dialnet Métricas: 1 Cita
• 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.