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Stream-Close: Fast mining of Closed Frequent Itemsets in high speed data streams

Ranganath, BN and Murty, Narasimha M (2008) Stream-Close: Fast mining of Closed Frequent Itemsets in high speed data streams. In: International Conference on Data Mining Workshops, 2008. ICDMW '08. IEEE , 15-19 Dec. 2008, Pisa .

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Abstract

With the emergence of large-volume and high-speed streaming data, the recent techniques for stream mining of CFIpsilas (closed frequent itemsets) will become inefficient. When concept drift occurs at a slow rate in high speed data streams, the rate of change of information across different sliding windows will be negligible. So, the user wonpsilat be devoid of change in information if we slide window by multiple transactions at a time. Therefore, we propose a novel approach for mining CFIpsilas cumulatively by making sliding width(ges1) over high speed data streams. However, it is nontrivial to mine CFIpsilas cumulatively over stream, because such growth may lead to the generation of exponential number of candidates for closure checking. In this study, we develop an efficient algorithm, stream-close, for mining CFIpsilas over stream by exploring some interesting properties. Our performance study reveals that stream-close achieves good scalability and has promising results.

Item Type: Conference Paper
Additional Information: Copyright 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Keywords: Data stream;CFI’s;Association rules.
Department/Centre: Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)
Date Deposited: 23 Sep 2011 09:33
Last Modified: 23 Sep 2011 09:33
URI: http://eprints.iisc.ernet.in/id/eprint/40668

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