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

International Journal of Interactive Multimedia and Artificial Intelligence

关键词: c-means;    clustering;    convergence;    jeffreys-divergence;    similarity measure;   

DOI  :  10.9781/ijimai.2021.04.009

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown