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From Data Mining To Behavior Mining

Author

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  • ZHENGXIN CHEN

    (Department of Computer Science, University of Nebraska at Omaha, Omaha, NE 68182-0500, USA)

Abstract

Knowledge economy requires data mining be more goal-oriented so that more tangible results can be produced. This requirement implies that the semantics of the data should be incorporated into the mining process. Data mining is ready to deal with this challenge because recent developments in data mining have shown an increasing interest on mining of complex data (as exemplified by graph mining, text mining, etc.). By incorporating the relationships of the data along with the data itself (rather than focusing on the data alone), complex data injects semantics into the mining process, thus enhancing the potential of making better contribution to knowledge economy. Since the relationships between the data reveal certain behavioral aspects underlying the plain data, this shift of mining from simple data to complex data signals a fundamental change to a new stage in the research and practice of knowledge discovery, which can be termed asbehavior mining. Behavior mining also has the potential of unifying some other recent activities in data mining. We discuss important aspects on behavior mining, and discuss its implications for the future of data mining.

Suggested Citation

  • Zhengxin Chen, 2006. "From Data Mining To Behavior Mining," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 5(04), pages 703-711.
  • Handle: RePEc:wsi:ijitdm:v:05:y:2006:i:04:n:s0219622006002271
    DOI: 10.1142/S0219622006002271
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    Citations

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    Cited by:

    1. Robin Gubela & Artem Bequé & Stefan Lessmann & Fabian Gebert, 2019. "Conversion Uplift in E-Commerce: A Systematic Benchmark of Modeling Strategies," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(03), pages 747-791, May.
    2. Gubela, Robin & Bequé, Artem & Gebert, Fabian & Lessmann, Stefan, 2018. "Conversion uplift in e-commerce: A systematic benchmark of modeling strategies," IRTG 1792 Discussion Papers 2018-062, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    3. Tagiew, Rustam & Ignatov, Dmitry I., 2017. "Behavior Mining in h-index Ranking Game," MPRA Paper 82795, University Library of Munich, Germany.

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