Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Constructive and algebraic methods of the theory of rough sets
Information Sciences: an International Journal
Unsupervised Rough Set Classification Using GAs
Journal of Intelligent Information Systems
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Genetic Algorithms
Information Granules in Distributed Environment
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Interval Set Clustering of Web Users with Rough K-Means
Journal of Intelligent Information Systems
Some refinements of rough k-means clustering
Pattern Recognition
Web Intelligence and Agent Systems
Rough Cluster Quality Index Based on Decision Theory
IEEE Transactions on Knowledge and Data Engineering
Rough multi-category decision theoretic framework
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Rough–Fuzzy Collaborative Clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolutionary and Iterative Crisp and Rough Clustering I: Theory
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Analysis of rough and fuzzy clustering
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Correlating Fuzzy and Rough Clustering
Fundamenta Informaticae - Rough Sets and Knowledge Technology (RSKT 2010)
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Rough K-means algorithm and its extensions have been useful in situations where clusters do not necessarily have crisp boundaries. Experimentation with the rough K-means algorithm has shown that it provides a reasonable set of lower and upper bounds for a given dataset. Evaluation of clustering obtained from rough K-means using various cluster validity measures has also been promising. However, rough K-means algorithm has not been explicitly shown to provide optimal rough clustering. This paper proposes an evolutionary rough K-means algorithm that minimizes a rough within-group-error. The proposal is different from previous Genetic Algorithms (GAs) based rough clustering, as it combines the efficiency of rough K-means algorithm with the optimization ability of GAs. The evolutionary rough K-means algorithm provides flexibility in terms of the optimization criterion. It can be used for optimizing rough clusters based on different criteria.