Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
Algorithms for clustering data
Algorithms for clustering data
On relational data versions of c-means algorithms
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
Fuzzy clustering in software reusability
Software—Practice & Experience
Partitioning-based clustering for Web document categorization
Decision Support Systems - Special issue on WITS '97
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Fuzzy clustering with partial supervision
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Alternating cluster estimation: a new tool for clustering and function approximation
IEEE Transactions on Fuzzy Systems
Generalized fuzzy c-means clustering strategies using Lp norm distances
IEEE Transactions on Fuzzy Systems
Interpretation of clusters in the framework of shadowed sets
Pattern Recognition Letters
Validation criteria for enhanced fuzzy clustering
Pattern Recognition Letters
Collaborative clustering with background knowledge
Data & Knowledge Engineering
IEEE Transactions on Neural Networks
Fuzzy clustering with viewpoints
IEEE Transactions on Fuzzy Systems
Data clustering with size constraints
Knowledge-Based Systems
Background knowledge integration in clustering using purity indexes
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
Granular computing with shadowed sets
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Enhanced fuzzy clustering algorithm and cluster validity index for human perception
Expert Systems with Applications: An International Journal
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Fuzzy clustering becomes a broadly accepted synonym of fundamental endeavors aimed at finding structures in multidimensional data. In essence, these methods operate in unsupervised mode. This means that they act upon data while being directed by some predefined objective function (criterion) for which they "discover" a structure (clusters) that yields a minimal value of this criterion. In this study, we discuss an issue of exploiting and effectively incorporating auxiliary problem dependent hints being available as a part of the domain knowledge associated with the pattern recognition problem at hand. As such hints are usually expressed by experts/data analysts at the level of clusters (information granules) rather than individual data (patterns), we refer to them as knowledge-based indicators and allude to a set of them as a knowledge-based guidance available to fuzzy clustering. The proposed paradigm shift in which fuzzy clustering incorporates this type of knowledge-based supervision is discussed and contrasted with the "pure" (that is data-driven) version of fuzzy clustering. Several fundamental categories of the guidance mechanisms are introduced and discussed, namely partial supervision, proximity-based guidance and uncertainty driven knowledge hints. The details on how the guidance machinery translates into updates of the partition matrices are presented. We also present a number of practical scenarios in which the role of knowledge hints becomes evident and highly justifiable. This concerns Web exploration, exploitation of labeled patterns, issues of incomplete feature spaces, and constraints of typicality of patterns, to name a few representative applications.