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.

通过使用参数熵以基于熵的质心初始化方法对图像数据进行 k-means 性能分析，并提出了适用于一般图像数据集的最佳熵测量，我们观察到对于不同的数据集，不同的熵比传统方法提供更好的结果。我们在 Satellite、Toys、Fruits、Cars、Brain MRI 和 Covid X-Ray 的数据集上应用了我们提出的算法。