Applied multivariate statistical analysis
Applied multivariate statistical analysis
Characterization and detection of noise in clustering
Pattern Recognition Letters
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Dimensionality Reduction of Unsupervised Data
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Improving fuzzy c-means clustering based on feature-weight learning
Pattern Recognition Letters
Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy c-means clustering of incomplete data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Generalized fuzzy c-means clustering strategies using Lp norm distances
IEEE Transactions on Fuzzy Systems
Optimization of clustering criteria by reformulation
IEEE Transactions on Fuzzy Systems
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The ReliefF algorithm is an important attribute weighting approach, which is built on the basis of classification labels. And the attribute weights of weighted FCM (WFCM), a popular fuzzy clustering algorithm, can be gotten by ReliefF. In the light of the idea of collaborative learning, a collaborative optimization of clustering by fuzzy c-means and weight determination by ReliefF (Co-WFCM) is introduced in this paper, in which FCM/WFCM and ReliefF who act as unsupervised and supervised learners are trained reciprocally. Experimental results show that the algorithm is helpful to get more satisfying clustering results and more rational attribute weights in some cases. And on the other hand, some suggestions for applicability of the ReliefF+FCM/WFCM algorithm framework can be given by analysis of the attribute weight sequences.