Tracking Clusters in Evolving Data Sets

Daniel Barbará and Ping Chen, George Mason University, USA

As organizations accumulate data over time, the problem of tracking how patterns evolve becomes important. In this paper, we present an algorithm to track the evolution of cluster models in a stream of data. Our algorithm is based on the application of bounds derived using Chernoff’s inequality and makes use of a clustering algorithm that was previously developed by us, namely Practal Clustering, which uses self-similarity as the property to group points together. Experiments show that our tracking algorithm is efficient and effective in finding changes on the patterns.


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