Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
Algorithms for clustering data
Algorithms for clustering data
A simple but powerful heuristic method for generating fuzzy rules from numerical data
Fuzzy Sets and Systems
Weighted fuzzy production rules
Fuzzy Sets and Systems
The three semantics of fuzzy sets
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Unsupervised feature selection using a neuro-fuzzy approach
Pattern Recognition Letters
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Fuzzy Sets and Systems: Theory and Applications
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A survey of fuzzy clustering algorithms for pattern recognition. I
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Improving fuzzy c-means clustering based on feature-weight learning
Pattern Recognition Letters
Real-Time Gesture Recognition by Learning and Selective Control of Visual Interest Points
IEEE Transactions on Pattern Analysis and Machine Intelligence
A k-mean clustering algorithm for mixed numeric and categorical data
Data & Knowledge Engineering
Comparison between two coevolutionary feature weighting algorithms in clustering
Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constrained locally weighted clustering
Proceedings of the VLDB Endowment
True color image steganography using palette and minimum spanning tree
CEA'09 Proceedings of the 3rd WSEAS international conference on Computer engineering and applications
International Journal of Computational Intelligence in Bioinformatics and Systems Biology
Theory of a probabilistic-dependence measure of dissimilarity among multiple clusters
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
A fuzzy k-prototype clustering algorithm for mixed numeric and categorical data
Knowledge-Based Systems
Fuzzy partition based soft subspace clustering and its applications in high dimensional data
Information Sciences: an International Journal
Test Pair Selection for Test Case Prioritization in Regression Testing for WS-BPEL Programs
International Journal of Web Services Research
Reliability assessment and failure analysis of lithium iron phosphate batteries
Information Sciences: an International Journal
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Similarity-based clustering is a simple but powerful technique which usually results in a clustering graph for a partitioning of threshold values in the unit interval. The guiding principle of similarity-based clustering is 驴similar objects are grouped in the same cluster.驴 To judge whether two objects are similar, a similarity measure must be given in advance. The similarity measure presented in this paper is determined in terms of the weighted distance between the features of the objects. Thus, the clustering graph and its performance (which is described by several evaluation indices defined in this paper) will depend on the feature weights. This paper shows that, by using gradient descent technique to learn the feature weights, the clustering performance can be significantly improved. It is also shown that our method helps to reduce the uncertainty (fuzziness and nonspecificity) of the similarity matrix. This enhances the quality of the similarity-based decision making.