Data Mining for Temporal Data

  • Wilfried Grossmann
  • Stefanie Rinderle-Ma
Chapter
Part of the Data-Centric Systems and Applications book series (DCSA)

Abstract

In this chapter, we present analysis techniques for temporal data. First of all, we discuss the different data structures in temporal mining, introduce the different analytical goals and models, and give an overview on the corresponding analytical techniques subsequently. Section 6.2 considers time warping and response feature analysis for clustering and classification, Sect. 6.3 discusses regression models and their role in predicting the time period until the occurrence of an event, and Sect. 6.4 introduces the analysis of Markov chains. The following sections deal with analysis techniques for temporal patterns, in particular association analysis, sequence mining, and episode mining.

Keywords

Markov Chain Association Rule Change Point Time Sequence Event Sequence 
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 2015

Authors and Affiliations

  • Wilfried Grossmann
    • 1
  • Stefanie Rinderle-Ma
    • 1
  1. 1.University of ViennaViennaAustria

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