A Stream Clustering Algorithm Using Information Theoretic Clustering Evaluation Function

Abstract

There are many stream clustering algorithms that can be divided roughly into density based algorithms and hyper spherical distance based algorithms. Only density based algorithms can detect nonlinear clusters and all algorithms assume that the data stream is an ordered sequence of points. Many algorithms need to receive data in buckets to start processing with online and offline iterations with several passes over the data. In this paper we propose a streaming clustering algorithm using a distance function which can separate highly nonlinear clusters in one pass. The distance function used is based on information theoretic measures and it is called Clustering Evaluation Function. The algorithm can handle data one point at a time and find the correct number of clusters even with highly nonlinear clusters. The data points can arrive in any random order and the number of clusters does not need to be specified. Each point is compared against already discovered clusters and each time clusters are joined or divided using an iteratively updated threshold.