Abstract
The pattern extraction is one of the main problem in data mining. Usually, patterns are defined by a conjunction of simple descriptors of the form (variable=value). In this paper we consider a more general form of descriptors, namely (variable∈valueset), where valueset is a subset of the variable domain. We present methods for generalized pattern extraction. We also investigate the problem of data table covering by a semi-optimal set of patterns. The proposed methods return more universal features satisfied by a large number of objects in data table (or in a given subset of objects). We also present applications of extracted patterns for two important tasks of KDD: prediction of new unseen cases and description of decision classes.
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Nguyen, S.H. (1999). Discovery of generalized patterns. In: RaÅ›, Z.W., Skowron, A. (eds) Foundations of Intelligent Systems. ISMIS 1999. Lecture Notes in Computer Science, vol 1609. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0095146
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DOI: https://doi.org/10.1007/BFb0095146
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