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.
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Grossmann, W., Rinderle-Ma, S. (2015). Data Mining for Temporal Data. In: Fundamentals of Business Intelligence. Data-Centric Systems and Applications. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46531-8_6
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DOI: https://doi.org/10.1007/978-3-662-46531-8_6
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