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      https://www.ias.ac.in/article/fulltext/sadh/031/02/0173-0198

    • Keywords

       

      Temporal data mining; ordered data streams; temporal interdependency; pattern discovery

    • Abstract

       

      Data mining is concerned with analysing large volumes of (often unstructured) data to automatically discover interesting regularities or relationships which in turn lead to better understanding of the underlying processes. The field of temporal data mining is concerned with such analysis in the case of ordered data streams with temporal interdependencies. Over the last decade many interesting techniques of temporal data mining were proposed and shown to be useful in many applications. Since temporal data mining brings together techniques from different fields such as statistics, machine learning and databases, the literature is scattered among many different sources. In this article, we present an overview of techniques of temporal data mining. We mainly concentrate on algorithms for pattern discovery in sequential data streams. We also describe some recent results regarding statistical analysis of pattern discovery methods.

    • Author Affiliations

       

      Srivatsan Laxman1 P S Sastry1

      1. Department of Electrical Engineering, Indian Institute of Science, Bangalore - 560 012, India
    • Dates

       
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