An efficient fractal dimension based clustering algorithm

Clustering plays an important role in data mining. It helps to reveal intrinsic structure in data sets with little or no prior knowledge. The approaches of clustering have received great attention in recent years. However many published algorithms fail to do well in determining the number of cluster, finding arbitrary shapes of clusters or identifying the presence of noise. In this paper we present an efficient clustering algorithm which employs the theory of grid, density and fractal that can partition points in the same cluster with minimum change of fractal dimension meanwhile maximizing the self-similarity in the clusters. We show via experiments that FDC can quickly deal with multidimensional large data sets, identify the number of clusters, be capable of recognizing clusters of arbitrary shape and furthermore explore some qualitative information from data sets.