A Fuzzy Density-based Incremental Clustering Algorithm

Abstract

This study presents a density-based incremental clustering algorithm which incorporates the concept of fuzzy set in clustering. Unlike other existing fuzzy clustering algorithms which are c-mean clustering where the number of clusters must be pre-defined, the proposed algorithm incorporates the concept of fuzzy set into density-based clustering where the number of clusters is not restricted. Moreover, the proposed algorithm uses incremental clustering usually employed in stream data clustering, leading to linear computation time, rather than quadratic computation time as in other density-based clustering. The proposed algorithm outperforms other existing density-based clustering algorithms in terms of both clustering results and computation time. As a result, the proposed algorithm can much efficiently process large data sets than other density-based clustering algorithms.