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WIREs Data Mining Knowl Discov
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An overview of emerging pattern mining in supervised descriptive rule discovery: taxonomy, empirical study, trends, and prospects

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Emerging pattern mining is a data mining task that aims to discover discriminative patterns, which can describe emerging behavior with respect to a property of interest. In recent years, the description of datasets has become an interesting field due to the easy acquisition of knowledge by the experts. In this review, we will focus on the descriptive point of view of the task. We collect the existing approaches that have been proposed in the literature and group them together in a taxonomy in order to obtain a general vision of the task. A complete empirical study demonstrates the suitability of the approaches presented. This review also presents future trends and emerging prospects within pattern mining and the benefits of knowledge extracted from emerging patterns.

General schema of a border‐based algorithm.
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Relationships among different types of emerging patterns (EPs).
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Comparison of the behavior of the three best methods against all analyzed quality measures.
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Comparison of the average Friedman rank of the algorithms against the three relevant aspects of the supervised descriptive rule discovery (SDRD) framework: interest, generality, and precision.
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General schema of an evolutionary fuzzy system (EFS) algorithm for the extraction of emerging patterns (EPs).
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General schema of the building of a decision tree for the extraction of emerging patterns (EPs).
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General algorithmic schema of a tree‐based algorithm.
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Fundamental Concepts of Data and Knowledge > Knowledge Representation
Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining

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