A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Interval Set Clustering of Web Users with Rough K-Means
Journal of Intelligent Information Systems
Knowledge-Based Clustering: From Data to Information Granules
Knowledge-Based Clustering: From Data to Information Granules
Some refinements of rough k-means clustering
Pattern Recognition
A clustering procedure for exploratory mining of vector time series
Pattern Recognition
A dynamic data granulation through adjustable fuzzy clustering
Pattern Recognition Letters
From fuzzy sets to shadowed sets: Interpretation and computing
International Journal of Intelligent Systems - Decision Sciences: Foundations and Applications
Shadowed c-means: Integrating fuzzy and rough clustering
Pattern Recognition
Evolutionary rough k-medoid clustering
Transactions on rough sets VIII
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shadowed sets: representing and processing fuzzy sets
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Analysis of the weighting exponent in the FCM
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 Set Based Generalized Fuzzy -Means Algorithm and Quantitative Indices
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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
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In this study, we develop a technique of an automatic selection of a threshold parameter, which determines approximation regions in rough set-based clustering. The proposed approach exploits a concept of shadowed sets. All patterns (data) to be clustered are placed into three categories assuming a certain perspective established by an optimization process. As a result, a lack of knowledge about global relationships among objects caused by the individual absolute distance in rough C-means clustering or individual membership degree in rough-fuzzy C-means clustering can be circumvented. Subsequently, relative approximation regions of each cluster are detected and described. By integrating several technologies of Granular Computing including fuzzy sets, rough sets, and shadowed sets, we show that the resulting characterization leads to an efficient description of information granules obtained through the process of clustering including their overlap regions, outliers, and boundary regions. Comparative experimental results reported for synthetic and real-world data illustrate the essence of the proposed idea.