@article{2941,
  keywords = {C-means, Clustering, Convergence, Jeffreys-Divergence, Similarity Measure},
  author = {Ayan Seal and Aditya Karlekar and Ondrej Krejcar and Enrique Herrera-Viedma},
  title = {Performance and Convergence Analysis of Modified C-Means Using Jeffreys-Divergence for Clustering},
  abstract = {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.},
  year = {2021},
  journal = {International Journal of Interactive Multimedia and Artificial Intelligence},
  volume = {7},
  number = {2},
  pages = {141-149},
  month = {12/2021},
  issn = {1989-1660},
  url = {https://www.ijimai.org/journal/sites/default/files/2021-11/ijimai7_2_13_0.pdf},
  doi = {10.9781/ijimai.2021.04.009},
}