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Knowledge Discovery from Databases (KDD) and Data Mining (DM) are general terms for a research area that deals with constructing models from data for predictive, descriptive or summarizing purposes. Existing text books provide a broad overview of these problems and usually devote most attention to topics such as clustering and classification. Their discussions of pattern mining are restricted to the basics of itemset mining and association rule discovery. However, pattern mining is more than frequent itemset mining. Pattern mining can be seen as the generalization of itemset mining to any task where a symbolic summary of data is needed. As a result of this, pattern mining is in fact at the core of many KDD problems but existing text books contrast those settings instead of relating them. Additionally, much progress in pattern mining has been made in recent years, in particular on how to select small sets of patterns and exploit patterns in other mining tasks, such as classification. This work is not yet organized in an easily accessible textbook. This book will organize all recent results in a pattern mining-centric way, hence providing a convenient, more advanced introduction to mining and using patterns.
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