Parametric entropy based Cluster Centriod Initialization for k-means clustering of various Image datasets
This work addresses the initialization bottleneck in k-means clustering for image analysis, offering incremental improvements in centroid selection for domain-specific applications.
The paper tackled the problem of k-means clustering performance on image datasets by proposing a parametric entropy-based centroid initialization method, finding that different entropies like Taneja and Kapur improved results over conventional methods across datasets such as Satellite and Brain MRI, though specific numerical gains were not provided.
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.