Parametric entropy based Cluster Centriod Initialization for k-means
  clustering of various Image datasets
Faheem Hussayn Shahid M Shah
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.