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Data stream clustering has attracted a huge attention in recent years. Many one-pass and evolving algorithms have been developed in this field but feature selection and its influence on clustering solution has not been addressed by these algorithms. In this paper we explain a feature-based clustering method for streaming data. Our method establishes a ranking between features based on their appropriateness in terms of clustering compactness and separateness. Then, it uses an automatic algorithm to identify unimportant features and remove them from feature set. These two steps take place continuously during lifetime of clustering task.
Date of Conference: 1-3 June 2009