Algorithms for clustering data
Algorithms for clustering data
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new cluster validity index for the fuzzy c-mean
Pattern Recognition Letters
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Cluster validity methods: part I
ACM SIGMOD Record
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Cluster validation techniques for genome expression data
Signal Processing - Special issue: Genomic signal processing
Stability-based validation of clustering solutions
Neural Computation
A knowledge-driven approach to cluster validity assessment
Bioinformatics
Resampling Method for Unsupervised Estimation of Cluster Validity
Neural Computation
An objective approach to cluster validation
Pattern Recognition Letters
On fuzzy cluster validity indices
Fuzzy Sets and Systems
Upper and lower values for the level of fuzziness in FCM
Information Sciences: an International Journal
Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability)
Validation criteria for enhanced fuzzy clustering
Pattern Recognition Letters
A cluster validity index for fuzzy clustering
Information Sciences: an International Journal
A currency crisis and its perception with fuzzy C-means
Information Sciences: an International Journal
A statistical model of cluster stability
Pattern Recognition
Development of assessment criteria for clustering algorithms
Pattern Analysis & Applications
Robust cluster validity indexes
Pattern Recognition
A new separation measure for improving the effectiveness of validity indices
Information Sciences: an International Journal
A stability based validity method for fuzzy clustering
Pattern Recognition
Cluster validation using information stability measures
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analysis of the weighting exponent in the FCM
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Enhanced Fuzzy System Models With Improved Fuzzy Clustering Algorithm
IEEE Transactions on Fuzzy Systems
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
Information Sciences: an International Journal
A new indirect approach to the type-2 fuzzy systems modeling and design
Information Sciences: an International Journal
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In this paper, we concentrate on the usage of uncertainty associated with the level of fuzziness in determination of the number of clusters in FCM for any data set. We propose a MiniMax @e-stable cluster validity index based on the uncertainty associated with the level of fuzziness within the framework of interval valued Type 2 fuzziness. If the data have a clustered structure, the optimum number of clusters may be assumed to have minimum uncertainty under upper and lower levels of fuzziness. Upper and lower values of the level of fuzziness for Fuzzy C-Mean (FCM) clustering methodology have been found as m=2.6 and 1.4, respectively, in our previous studies. Our investigation shows that the stability of cluster centers with respect to the level of fuzziness is sufficient for the determination of the number of clusters.