k-Intervals: A New Extension of the k-Means Algorithm

In this paper we propose a new extension of the k-means algorithm by changing the model it uses to represent clusters. The objectives of traditional k-means and lots of other k-means-related clustering algorithms are all center-based. We suggest using an alternative way to represent the clusters while computing the similarity between an object and a certain cluster. The purpose is to preserve more information of the clusters and at the same time keep the simplicity of the algorithm. In this paper we use intervals to represent clusters and propose a new clustering algorithm k-intervals based on this model. Experimental results on both synthetic data sets and real data sets (several UCI data sets and the ORL face database) demonstrate the effectiveness and the advantages of the proposed algorithm.