Parametric entropy based Cluster Centriod Initialization for k-means clustering of various Image datasets

TL;DR

This paper introduces a novel centroid initialization method for k-means clustering using parametric entropies, improving clustering quality across various image datasets by selecting the most suitable entropy measure.

Contribution

The paper proposes an entropy-based centroid initialization technique utilizing multiple parametric entropies, tailored for different image datasets to enhance k-means clustering performance.

Findings

Different entropies perform best on different datasets.

The proposed method improves clustering results over traditional initialization.

Effective for diverse image datasets like MRI, X-Ray, and satellite images.

Abstract

One of the most employed yet simple algorithm for cluster analysis is the k-means algorithm. k-means has successfully witnessed its use in artificial intelligence, market segmentation, fraud detection, data mining, psychology, etc., only to name a few. The k-means algorithm, however, does not always yield the best quality results. Its performance heavily depends upon the number of clusters supplied and the proper initialization of the cluster centroids or seeds. In this paper, we conduct an analysis of the performance of k-means on image data by employing parametric entropies in an entropy based centroid initialization method and propose the best fitting entropy measures for general image datasets. We use several entropies like Taneja entropy, Kapur entropy, Aczel Daroczy entropy, Sharma Mittal entropy. We observe that for different datasets, different entropies provide better results than the conventional methods. We have applied our proposed algorithm on these datasets: Satellite, Toys, Fruits, Cars, Brain MRI, Covid X-Ray.