Algorithms for clustering data
Algorithms for clustering data
Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Validating fuzzy partitions obtained through c-shells clustering
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
ACM Computing Surveys (CSUR)
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Cluster validity methods: part I
ACM SIGMOD Record
Stability-based validation of clustering solutions
Neural Computation
CURLER: finding and visualizing nonlinear correlation clusters
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Resampling Method for Unsupervised Estimation of Cluster Validity
Neural Computation
An objective approach to cluster validation
Pattern Recognition Letters
Pattern Recognition Letters
On fuzzy cluster validity indices
Fuzzy Sets and Systems
Validation criteria for enhanced fuzzy clustering
Pattern Recognition Letters
A sober look at clustering stability
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Analysis of the weighting exponent in the FCM
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Correction to "On Cluster Validity for the Fuzzy c-Means Model" [Correspondence]
IEEE Transactions on Fuzzy Systems
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
Pattern Recognition Letters
A validity criterion for fuzzy clustering
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part I
MiniMax ε-stable cluster validity index for Type-2 fuzziness
Information Sciences: an International Journal
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An important goal in cluster analysis is the internal validation of results using an objective criterion. Of particular relevance in this respect is the estimation of the optimum number of clusters capturing the intrinsic structure of your data. This paper proposes a method to determine this optimum number based on the evaluation of fuzzy partition stability under bootstrap resampling. The method is first characterized on synthetic data with respect to hyper-parameters, like the fuzzifier, and spatial clustering parameters, such as feature space dimensionality, clusters degree of overlap, and number of clusters. The method is then validated on experimental datasets. Furthermore, the performance of the proposed method is compared to that obtained using a number of traditional fuzzy validity rules based on the cluster compactness-to-separation criteria. The proposed method provides accurate and reliable results, and offers better generalization capabilities than the classical approaches.