Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Fuzzy clustering with a knowledge-based guidance
Pattern Recognition Letters
Improving Fuzzy C-Means with Shadow Set
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 01
Fuzzy c-means clustering with prior biological knowledge
Journal of Biomedical Informatics
A new fuzzy clustering algorithm for optimally finding granular prototypes
International Journal of Approximate Reasoning
On cluster-wise fuzzy regression analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Supervised fuzzy clustering for rule extraction
IEEE Transactions on Fuzzy Systems
Semantic Web Content Analysis: A Study in Proximity-Based Collaborative Clustering
IEEE Transactions on Fuzzy Systems
Data clustering with size constraints
Knowledge-Based Systems
New results on a fuzzy granular space
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
Spectral clustering with discriminant cuts
Knowledge-Based Systems
Granular fuzzy models: a study in knowledge management in fuzzy modeling
International Journal of Approximate Reasoning
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In this study, we introduce a certain knowledgeguided scheme of fuzzy clustering in which domain knowledge is represented in the form of so-called viewpoints. Viewpoints capture a way in which the user introduces his/her point of view at the data by identifying some representatives, which, being treated as externally introduced prototypes, have to be included in the clustering process. More formally, the viewpoints (views) augment the original, data-based objective function by including the term that expresses distances between data and the viewpoints. Depending upon the nature of domain knowledge, the viewpoints are represented either in a plain numeric format (considering that there is a high level of specificity with regard to how one establishes perspective from which the data need to be analyzed) or through some information granules (which reflect a more relaxed way in which the views at the data are being expressed). The detailed optimization schemes are presented, and the performance of the method is illustrated through some numeric examples. We also elaborate on a way in which the clustering with viewpoints enhances fuzzy models and mechanisms of decision making in the sense that the resulting constructs reflect the preferences and requirement that are present in the modeling environment.