Performance analysis of k-means with different initialization methods for high dimensional data

Abstract

Developing effective clustering method for high dimensional dataset is a challenging problem due to the curse of dimensionality. Among all the partition based clustering algorithms, k-means is one of the most well known methods to partition a dataset into groups of patterns. However, the k-means method converges to one of many local minima. And it is known that, the final result depends on the initial starting points (means). Many methods have been proposed to improve the performance of k-means algorithm. In this paper, we have analyzed the performance of our proposed method with the existing works. In our proposed method, we have used Principal Component Analysis (PCA) for dimension reduction and to find the initial centroid for k-means. Next we have used heuristics approach to reduce the number of distance calculation to assign the data point to cluster. By comparing the results on iris data set, it was found that the results obtained by the proposed method are more effective than the existing method.

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