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Detection And Classification Of Changes In Evolving Data Streams

Author

Listed:
  • 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)

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.

Suggested Citation

  • Mohamed Medhat Gaber & Philip S. Yu, 2006. "Detection And Classification Of Changes In Evolving Data Streams," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 5(04), pages 659-670.
    • Handle: RePEc:wsi:ijitdm:v:05:y:2006:i:04:n:s0219622006002179
    DOI: 10.1142/S0219622006002179
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    Cited by:

    1. Ravi Sankar Sangam & Hari Om, 2018. "Equi-Clustream: a framework for clustering time evolving mixed data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(4), pages 973-995, December.

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