An overview of emerging pattern mining in supervised descriptive rule discovery: taxonomy, empirical study, trends, and prospects

TLDR

Emerging pattern mining discovers discriminative patterns that describe emerging behavior relative to a property, and dataset description has become a compelling field due to easy knowledge acquisition by experts. This review focuses on the descriptive perspective of emerging pattern mining, outlining future trends and the benefits of knowledge extracted from emerging patterns. The authors compiled existing approaches and organized them into a taxonomy to provide a comprehensive view of the task. An empirical study confirms the suitability of the presented approaches, while the review highlights future trends and the value of extracted knowledge. WIREs Data Mining Knowledge Discovery 2018, 8:e1231, doi:10.1002/widm.1231, categorized under Fundamental Concepts of Data and Knowledge.

Abstract

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. WIREs Data Mining Knowl Discov 2018, 8:e1231. doi: 10.1002/widm.1231 This article is categorized under: Fundamental Concepts of Data and Knowledge > Knowledge Representation Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining