Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
On Clustering Validation Techniques
Journal of Intelligent Information Systems
The Amsterdam Library of Object Images
International Journal of Computer Vision
A cluster validity index for fuzzy clustering
Pattern Recognition Letters
Pattern Recognition Letters
Practical Algorithms for Image Analysis with CD-ROM
Practical Algorithms for Image Analysis with CD-ROM
ACM Transactions on Knowledge Discovery from Data (TKDD)
Relative clustering validity criteria: A comparative overview
Statistical Analysis and Data Mining
Comparing fuzzy, probabilistic, and possibilistic partitions
IEEE Transactions on Fuzzy Systems
Evolutionary clustering of relational data
International Journal of Hybrid Intelligent Systems - Advances in Intelligent Agent Systems
Evolving clusters in gene-expression data
Information Sciences: an International Journal
Low-complexity fuzzy relational clustering algorithms for Web mining
IEEE Transactions on Fuzzy Systems
Reducing the time complexity of the fuzzy c-means algorithm
IEEE Transactions on Fuzzy Systems
Fuzzy Clustering and Aggregation of Relational Data With Instance-Level Constraints
IEEE Transactions on Fuzzy Systems
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
On the combination of relative clustering validity criteria
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
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The attributes describing a data set may often be arranged in meaningful subsets, each of which corresponds to a different aspect of the data. An unsupervised algorithm (SCAD) that simultaneously performs fuzzy clustering and aspects weighting was proposed in the literature. However, SCAD may fail and halt given certain conditions. To fix this problem, its steps are modified and then reordered to reduce the number of parameters required to be set by the user. In this paper we prove that each step of the resulting algorithm, named ASCAD, globally minimizes its cost-function with respect to the argument being optimized. The asymptotic analysis of ASCAD leads to a time complexity which is the same as that of fuzzy c-means. A hard version of the algorithm and a novel validity criterion that considers aspect weights in order to estimate the number of clusters are also described. The proposed method is assessed over several artificial and real data sets.