Mining Closed Regular Patterns in Data Streams

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Mining regular patterns in data streams is an emerging research area and also a challenging problem in present days because in Data streams new data comes continuously with varying rates. Closed item set mining gained lot of implication in data mining research from conventional mining methods. So in this paper we propose a narrative approach called CRPDS (Closed Regular Patterns in Data Streams) with vertical data format using sliding window model. To our knowledge no method has been proposed to mine closed regular patterns in data streams. As the stream flows our CRPDS-method mines closed regular itemsets based on regularity threshold and user given support count. The experimental results show that the proposed method is efficient and scalable in terms of memory and time.