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Skypattern mining: From pattern condensed representations to dynamic constraint satisfaction problems
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DOI：10.1016/j.artint.2015.04.003.png)
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Abstract
En 中文
Data mining is the study of how to extract information from data and express it as useful knowledge. One of its most important subfields, pattern mining, involves searching and enumerating interesting patterns in data. Various aspects of pattern mining are studied in the theory of computation and statistics. In the last decade, the pattern mining community has witnessed a sharp shift from efficiency-based approaches to methods which can extract more meaningful patterns. Recently, new methods adapting results from studies of economic efficiency and multi-criteria decision analyses such as Pareto efficiency, or skylines, have been studied. Within pattern mining, this novel line of research allows the easy expression of preferences according to a dominance relation. This approach is useful from a user-preference point of view and tends to promote the use of pattern mining algorithms for non-experts. We present a significant extension of our previous work [1,2] on the discovery of skyline patterns (or skypatterns) based on the theoretical relationships with condensed representations of patterns. We show how these relationships facilitate the computation of skypattems and we exploit them to propose a flexible and efficient approach to mine skypattems using a dynamic constraint satisfaction problems (CSP) framework. We present a unified methodology of our different approaches towards the same goal. This work is supported by an extensive experimental study allowing us to illustrate the strengths and weaknesses of each approach. (C) 2015 Elsevier B.V. All rights reserved.
Keywords:
Skypatterns
Pattern mining
Constraint programming
Dynamic CSP
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