DETECTION AND CLASSIFICATION OF CHANGES IN EVOLVING DATA STREAMS

Mohamed Medhat Gaber () and Philip S. Yu ()
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Mohamed Medhat Gaber: School of Information Technologies, University of Sydney, NSW 2006, Australia
Philip S. Yu: IBM Thomas J. Watson Research Center, 19, Skyline Drive, Hawthorne, NY 10532, USA

International Journal of Information Technology & Decision Making (IJITDM), 2006, vol. 05, issue 04, 659-670

Abstract: Data stream mining has attracted considerable attention over the past few years owing to the significance of its applications. Streaming data is often evolving over time. Capturing changes could be used for detecting an event or a phenomenon in various applications. Weather conditions, economical changes, astronomical, and scientific phenomena are among a wide range of applications. Because of the high volume and speed of data streams, it is computationally hard to capture these changes from raw data in real-time. In this paper, we propose a novel algorithm that we term as STREAM-DETECT to capture these changes in data stream distribution and/or domain using clustering result deviation. STREAM-DETECT is followed by a process of offline classification CHANGE-CLASS. This classification is concerned with the association of the history of change characteristics with the observed event or phenomenon. Experimental results show the efficiency of the proposed framework in both detecting the changes and classification accuracy.

Keywords: Data streams; change detection; classification and clustering (search for similar items in EconPapers)
Date: 2006
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S0219622006002179

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