Algorithms for clustering data
Algorithms for clustering data
Adaptive pattern recognition and neural networks
Adaptive pattern recognition and neural networks
Symbolic clustering using a new dissimilarity measure
Pattern Recognition
Fuzzy logic, neural networks, and soft computing
Communications of the ACM
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing
Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing
Data Mining: Concepts and Algorithms From Multimedia to Bioinformatics
Data Mining: Concepts and Algorithms From Multimedia to Bioinformatics
Clustering and its validation in a symbolic framework
Pattern Recognition Letters
Fast learning in networks of locally-tuned processing units
Neural Computation
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Low-complexity fuzzy relational clustering algorithms for Web mining
IEEE Transactions on Fuzzy Systems
A Symbolic Pattern Classifier for Interval Data Based on Binary Probit Analysis
KI '08 Proceedings of the 31st annual German conference on Advances in Artificial Intelligence
A modal symbolic classifier for interval data
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Expert Systems with Applications: An International Journal
| Hi-index | 0.21 |
Symbolic fuzzy classification is proposed using fuzzy radial basis function network, with fuzzy c-medoids clustering at the hidden layer. Symbolic objects include linguistic, nominal, boolean and interval-type of features, along with quantitative attributes. Classification and clustering in this domain involve the use of symbolic dissimilarity between the objects. Fuzzy memberships are used for appropriately handling uncertainty inherent in real-life decisions. The fuzzy radial basis function (FRBF) network here comprises an integration of the principles of radial basis function (RBF) network and fuzzy c-medoids clustering, for handling non-numeric data. The optimal number of hidden nodes is determined by using clustering validity indices, like normalized modified Hubert's statistic and Davies-Bouldin index, in the symbolic framework. The effectiveness of the symbolic fuzzy classification is demonstrated on real-life benchmark data sets. Comparison is provided with the performance of a decision tree.