, Volume 31, Issue 2, pp 173–198

A survey of temporal data mining

  • Srivatsan Laxman
  • P. S. Sastry

DOI: 10.1007/BF02719780

Cite this article as:
Laxman, S. & Sastry, P.S. Sadhana (2006) 31: 173. doi:10.1007/BF02719780


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.


Temporal data mining ordered data streams temporal interdependency pattern discovery 

Copyright information

© Indian Academy of Sciences 2006

Authors and Affiliations

  • Srivatsan Laxman
    • 1
  • P. S. Sastry
    • 1
  1. 1.Department of Electrical EngineeringIndian Institute of ScienceBangaloreIndia

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