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Local Pattern Detection pp 98–114Cite as

Features for Learning Local Patterns in Time-Stamped Data

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3539))

Abstract

Time-stamped data occur frequently in real-world databases. The goal of analysing time-stamped data is very often to find a small group of objects (customers, machine parts,...) which is important for the business at hand. In contrast, the majority of objects obey well-known rules and is not of interest for the analysis. In terms of a classification task, the small group means that there are very few positive examples and within them, there is some sort of a structure such that the small group differs significantly from the majority. We may consider such a learning task learning a local pattern.

Depending on the goal of the data analysis, different aspects of time are relevant, e.g., the particular date, the duration of a certain state, or the number of different states. From the given data, we may generate features that allow us to express the aspect of interest. Here, we investigate the aspect of state change and its representation for learning local patterns in time-stamped data. Besides a simple Boolean representation indicating a change, we use frequency features from information retrieval. We transfer Joachim’s theory for text classification to our task and investigate its fit to local pattern learning. The approach has been implemented within the MiningMart system and was successfully applied to real-world insurance data.

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Author information

Authors and Affiliations

  1. Computer Science Department, LS VIII, Univ. Dortmund,  

    Katharina Morik & Hanna Köpcke

Authors
  1. Katharina Morik
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  2. Hanna Köpcke
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Editor information

Editors and Affiliations

  1. Computer Science VIII, artificial Intelligence Unit, Technische Universität Dortmund, 44221, Dortmund, Germany

    Katharina Morik

  2. INSA-Lyon, LIRIS CNRS UMR5205, F-69621, Villeurbanne, France

    Jean-François Boulicaut

  3. Department of Computer Science, Universiteit Utrecht,  

    Arno Siebes

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© 2005 Springer-Verlag Berlin Heidelberg

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Morik, K., Köpcke, H. (2005). Features for Learning Local Patterns in Time-Stamped Data. In: Morik, K., Boulicaut, JF., Siebes, A. (eds) Local Pattern Detection. Lecture Notes in Computer Science(), vol 3539. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11504245_7

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  • DOI: https://doi.org/10.1007/11504245_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26543-6

  • Online ISBN: 978-3-540-31894-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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