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LeaDen-Stream: A Leader Density-Based Clustering Algorithm over Evolving Data Stream

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DOI: 10.4236/jcc.2013.15005    3,499 Downloads   6,243 Views   Citations

ABSTRACT

Clustering evolving data streams is important to be performed in a limited time with a reasonable quality. The existing micro clustering based methods do not consider the distribution of data points inside the micro cluster. We propose LeaDen-Stream (Leader Density-based clustering algorithm over evolving data Stream), a density-based clustering algorithm using leader clustering. The algorithm is based on a two-phase clustering. The online phase selects the proper mini-micro or micro-cluster leaders based on the distribution of data points in the micro clusters. Then, the leader centers are sent to the offline phase to form final clusters. In LeaDen-Stream, by carefully choosing between two kinds of micro leaders, we decrease time complexity of the clustering while maintaining the cluster quality. A pruning strategy is also used to filter out real data from noise by introducing dense and sparse mini-micro and micro-cluster leaders. Our performance study over a number of real and synthetic data sets demonstrates the effectiveness and efficiency of our method.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Amini, A. and Wah, T. (2013) LeaDen-Stream: A Leader Density-Based Clustering Algorithm over Evolving Data Stream. Journal of Computer and Communications, 1, 26-31. doi: 10.4236/jcc.2013.15005.

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