Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Neural Network Models: Theory and Projects
Neural Network Models: Theory and Projects
Interval Set Clustering of Web Users with Rough K-Means
Journal of Intelligent Information Systems
Some refinements of rough k-means clustering
Pattern Recognition
Fast learning in networks of locally-tuned processing units
Neural Computation
Fuzzy Sets and Systems
Comparison of conventional and rough K-means clustering
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Outliers in rough k-means clustering
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
A partitive rough clustering algorithm
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Rough–Fuzzy Collaborative Clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Dynamic Approach to Rough Clustering
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Evolutionary and Iterative Crisp and Rough Clustering I: Theory
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Shadowed sets in the characterization of rough-fuzzy clustering
Pattern Recognition
Projected Gustafson-Kessel clustering algorithm and its convergence
Transactions on rough sets XIV
Dynamic rough clustering and its applications
Applied Soft Computing
Soft clustering -- Fuzzy and rough approaches and their extensions and derivatives
International Journal of Approximate Reasoning
An extension to rough c-means clustering algorithm based on boundary area elements discrimination
Transactions on Rough Sets XVI
An extension to Rough c-means clustering based on decision-theoretic Rough Sets model
International Journal of Approximate Reasoning
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Recently, clustering algorithms based on rough set theory have gained increasing attention. For example, Lingras et al. introduced a rough k-means that assigns objects to lower and upper approximations of clusters. The objects in the lower approximation surely belong to a cluster while the membership of the objects in an upper approximation is uncertain. Therefore, the core cluster, defined by the objects in the lower approximation is surrounded by a buffer or boundary set with objects with unclear membership status. In this paper, we introduce an evolutionary rough k-medoid clustering algorithm. Evolutionary rough k-medoid clustering belongs to the families of Lingras' rough k-means and classic k-medoids algorithms. We apply the evolutionary rough k-medoids to synthetic as well as to real data sets and compare the results to Lingras' rough k-means. We also introduce a rough version of the Davies-Bouldin-Index as a cluster validity index for the family of rough clustering algorithms.