MIC-KMeans: A maximum information coefficient based high-dimensional clustering algorithm

Abstract

Clustering algorithms often use distance measure as the measure of similarity between point pairs. Such clustering algorithms are difficult to deal with the curse of dimensionality in high-dimension space. In order to address this issue which is common in clustering algorithms, we proposed to use MIC instead of distance measure in k-means clustering algorithm and implemented the novel MIC-kmeans algorithm for high-dimension clustering. MIC-kmeans can cluster the data with correlation to avoid the problem of distance failure in high-dimension space. The experimental results over the synthetic data and real datasets show that MIC-kmeans is superior to k-means clustering algorithm based on distance measure.