Data mining: concepts and techniques
Data mining: concepts and techniques
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Non-convex clustering using expectation maximization algorithm with rough set initialization
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
A Rough Set-Based Clustering Method with Modification of Equivalence Relations
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Interval Set Clustering of Web Users with Rough K-Means
Journal of Intelligent Information Systems
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In many data mining applications, cluster analysis is widely used and its results are expected to be interpretable, comprehensible, and usable. Rough set theory is one of the techniques to induce decision rules and manage inconsistent and incomplete information. This paper proposes a method to construct equivalence classes during the clustering process, isolate outlier points and finally deduce a rough set model from the clustering results. By the rough set model, attribute reduction and decision rule induction can be implemented efficiently and effectively. Experiments on real world data show that our method is useful and robust in handling data with noise.