Monitoring Change in Mining Results

  • Steffan Baron
  • Myra Spiliopoulou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2114)


In the last years the datasets available have grown tremendously, and the development of efficient and scalable data mining algorithms has become a major research challenge. However, since the data is more dynamic than static there is also a strong need to update previously discovered rules and patterns. Recently, a couple of studies have emerged dealing with the topic of incremental update of discovered knowledge. These studies mostly concentrate on the question whether new rules emerge or old ones become extinct.

We present a framework that enables the analyst to monitor the changes a rule may undergo when the dataset the rules were discovered from is updated, and to observe emerging trends as data change. We propose a generic rule model that distinguishes between different types of pattern changes, and provide formal definitions for these. We present our approach in a case study on the evolution of web usage patterns. These patterns have been stored in a database and are used to observe the mining sessions as snapshots across the time series of a patterns lifetime.


Association Rule Mining Session Mining Result Temporal Object Navigation Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Steffan Baron
    • 1
  • Myra Spiliopoulou
    • 2
  1. 1.Institut für WirtschaftsinformatikHumboldt-Universität zu BerlinBerlin
  2. 2.Institute of Business and Technical Information SystemsOtto-von-Guericke Universität MagdeburgMagdeburg

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