Clustering based on correlation fractal dimension over an evolving data stream

Online clustering, in an evolving high dimensional data is an amazing challenge for data mining applications. Although, many clustering strategies have been proposed, it is still an exciting task since the published algorithms fail to do well with high dimensional datasets, finding arbitrary shaped clusters and handling outliers. Knowing fractal characteristics of dataset can help abstract the dataset and provide insightful hints in the clustering process. This paper concentrates on presenting a novel strategy, FractStream for clustering data streams using fractal dimension, basic window technology, and damped window model. Core fractal-clusters, progressive fractal-cluster, outlier fractal clusters are identified, aiming to reduce search complexity and execution time. Pruning strategies are also employed based on the weights associated with each cluster, which reduced the usage of main memory. Experimental study of this paper over a number of data sets demonstrates the effectiveness and efficiency of the proposed technique.