Multidimensional similarity structure analysis
Multidimensional similarity structure analysis
Convergence theory for fuzzy c-means: counterexamples and repairs
IEEE Transactions on Systems, Man and Cybernetics
Algorithms for clustering data
Algorithms for clustering data
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Fuzzy C Quadratic Shell clustering algorithm and the detection of second-degree curves
Pattern Recognition Letters
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Validating fuzzy partitions obtained through c-shells clustering
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
Visualizing Data
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Analysis of Planar Shapes Using Geodesic Paths on Shape Spaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy Measures on the Gene Ontology for Gene Product Similarity
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Scalable visual assessment of cluster tendency for large data sets
Pattern Recognition
On fuzzy cluster validity indices
Fuzzy Sets and Systems
Linguistic summarization of video for fall detection using voxel person and fuzzy logic
Computer Vision and Image Understanding
Finding the number of clusters in ordered dissimilarities
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on ICNC-FSKD’2008;Guest Editors: Liang Zhao, Maozu Guo, Lipo Wang
Modeling human activity from voxel person using fuzzy logic
IEEE Transactions on Fuzzy Systems
bigVAT: Visual assessment of cluster tendency for large data sets
Pattern Recognition
iVAT and aVAT: enhanced visual analysis for cluster tendency assessment
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The possibilistic C-means algorithm: insights and recommendations
IEEE Transactions on Fuzzy Systems
Low-complexity fuzzy relational clustering algorithms for Web mining
IEEE Transactions on Fuzzy Systems
A Possibilistic Fuzzy c-Means Clustering Algorithm
IEEE Transactions on Fuzzy Systems
Visual Assessment of Clustering Tendency for Rectangular Dissimilarity Matrices
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Computing with words with the ontological self-organizing map
IEEE Transactions on Fuzzy Systems - Special section on computing with words
Relational duals of cluster-validity functions for the c-means family
IEEE Transactions on Fuzzy Systems
New results on a fuzzy granular space
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
Thematic fuzzy clusters with an additive spectral approach
EPIA'11 Proceedings of the 15th Portugese conference on Progress in artificial intelligence
Combining clustering and SVM for automatic modulation classification
International Journal of Computer Applications in Technology
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Numerous computational schemes have arisen over the years that attempt to learn information about objects based upon the similarity or dissimilarity of one object to another. One such scheme, clustering, looks for self-similar groups of objects. To use clustering algorithms, an investigator must often have a priori knowledge of the number of clusters, i.e., c, to search for in the data. Moreover, it is often convenient to have ways to rank the returned results, either for a single value of c, a range of c's different clustering methods, or any combination thereof. However, the task of assessing the quality of the results, so that c may be determined objectively, is currently ill-defined for object-object relationships. To bridge this gap, we generalize three well-known validity indices: the modified Hubert's Gamma, Xie-Beni, and the generalized Dunn's indices, to relational data. In doing so, we develop a framework to convert many other validity indices to a relational form. Numerical examples on 12 datasets (samples from four normal mixtures, four real-world object datasets, and four real-world "pure relational" datasets) using the relational duals of the hard, fuzzy, and possibilistic c-means cluster algorithms are offered to illustrate and evaluate the new indices.