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
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
Attribute reduction in decision-theoretic rough set models
Information Sciences: an International Journal
Constructive and algebraic methods of the theory of rough sets
Information Sciences: an International Journal
Fuzzy clustering with partial supervision
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Rough–Fuzzy Collaborative Clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Rough fuzzy MLP: knowledge encoding and classification
IEEE Transactions on Neural Networks
Evolutionary and Iterative Crisp and Rough Clustering I: Theory
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Transactions on rough sets XII
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)
International Journal of Approximate Reasoning
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Conventional clustering algorithms categorize an object into precisely one cluster. In many applications, the membership of some of the objects to a cluster can be ambiguous. Therefore, an ability to specify membership to multiple clusters can be useful in real world applications. Fuzzy clustering makes it possible to specify the degree to which a given object belongs to a cluster. In Rough set representations, an object may belong to more than one cluster, which is more flexible than the conventional crisp clusters and less verbose than the fuzzy clusters. The unsupervised nature of fuzzy and rough algorithms means that there is a choice about the level of precision depending on the choice of parameters. This paper describes how one can vary the precision of the rough set clustering and studies its effect on synthetic and real world data sets.