Directional fuzzy clustering and its application to fuzzy modelling
Fuzzy Sets and Systems
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
Image segmentation by clustering of spatial patterns
Pattern Recognition Letters
New modifications and applications of fuzzy C-means methodology
Computational Statistics & Data Analysis
A currency crisis and its perception with fuzzy C-means
Information Sciences: an International Journal
A new segmentation system for brain MR images based on fuzzy techniques
Applied Soft Computing
Novel modified fuzzy c-means algorithm with applications
Digital Signal Processing
Detecting and measuring rings in banknote images
Engineering Applications of Artificial Intelligence
Toward a Theory of Granular Computing for Human-Centered Information Processing
IEEE Transactions on Fuzzy Systems
Conditional fuzzy clustering in the design of radial basis function neural networks
IEEE Transactions on Neural Networks
Information Sciences: an International Journal
Intuitionistic fuzzy hypergraphs with applications
Information Sciences: an International Journal
Granular computing for relational data classification
Journal of Intelligent Information Systems
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Fuzzy clustering being focused on the discovery of structure in multivariable data is of relational nature in the sense of not distinguishing between the natures of the individual variables (features) encountered in the problem. In this study, we revisit the generic approach to clustering by studying situations in which there are families of features of descriptive and functional nature whose semantics needs to be incorporated into the clustering algorithm. While the structure is determined on the basis of all features taken en-block, it is anticipated that the topology revealed in this manner would aid the effectiveness of determining values of functional features given the vector of the corresponding descriptive features. We propose an augmented distance in which the families of descriptive and predictive features are distinguished through some weighted version of the distance between patterns. The optimization of this distance is guided by a reconstruction criterion, which helps minimize the reconstruction error between the original vector of functional features and their reconstruction realized by means of descriptive features. Experimental results are offered to demonstrate the performance of the clustering and quantify the effect of reaching balance between semantically distinct families of features.