Unsupervised fuzzy clustering with multi-center clusters
Fuzzy Sets and Systems - Clustering and modeling
Three-way fuzzy clustering models for LR fuzzy time trajectories
Computational Statistics & Data Analysis
Clustering interval-valued proximity data using belief functions
Pattern Recognition Letters
Clustering of interval data based on city-block distances
Pattern Recognition Letters
Text processing simplified ARTMAP neural network
SEPADS'05 Proceedings of the 4th WSEAS International Conference on Software Engineering, Parallel & Distributed Systems
On the efficiency of evolutionary fuzzy clustering
Journal of Heuristics
Intermediate variable normalization for gradient descent learning for hierarchical fuzzy system
IEEE Transactions on Fuzzy Systems
Clustering of symbolic data using the assignment-prototype algorithm
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
On fuzzy cluster validity indices for the objects of mixed features
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
A partitioning method for mixed feature-type symbolic data using a squared Euclidean distance
KI'06 Proceedings of the 29th annual German conference on Artificial intelligence
A modified SOM learning algorithm for mixed types of symbolic and fuzzy data
MMACTEE'09 Proceedings of the 11th WSEAS international conference on Mathematical methods and computational techniques in electrical engineering
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Algorithm for fuzzy clustering of mixed data with numeric and categorical attributes
ICDCIT'05 Proceedings of the Second international conference on Distributed Computing and Internet Technology
Self-organizing map for symbolic data
Fuzzy Sets and Systems
Fuzzy Kohonen clustering networks for interval data
Neurocomputing
Clustering interval data through kernel-induced feature space
Journal of Intelligent Information Systems
International Journal of Artificial Life Research
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Computational intelligence models for image processing and information reasoning
Hi-index | 0.00 |
Most of the techniques used in the literature in clustering symbolic data are based on the hierarchical methodology, which utilizes the concept of agglomerative or divisive methods as the core of the algorithm. The main contribution of this paper is to show how to apply the concept of fuzziness on a data set of symbolic objects and how to use this concept in formulating the clustering problem of symbolic objects as a partitioning problem. Finally, a fuzzy symbolic c-means algorithm is introduced as an application of applying and testing the proposed algorithm on real and synthetic data sets. The results of the application of the new algorithm show that the new technique is quite efficient and, in many respects, superior to traditional methods of hierarchical nature