A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Data mining: concepts and techniques
Data mining: concepts and techniques
Discriminative Clustering: Optimal Contingency Tables by Learning Metrics
ECML '02 Proceedings of the 13th European Conference on Machine Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Supervised Clustering " Algorithms and Benefits
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
General C-Means Clustering Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Data clustering is an important part of cluster analysis. Numerous semi-supervised or supervised clustering algorithms based on various theories have been developed, and new clustering algorithms continue to appear in the literature. The problem of common supervised clustering is to train a clustering algorithm to produce desirable clusters and complete clusters over datasets and learn how to cluster future sets of objects. In this paper, we have proposed an algorithm called Supervised Gravitational Clustering based on bipolar fuzzification. Traditional supervised clustering methods identify class-uniform clusters; but the offered method identifies class-multiform clusterswith high probability densities. For this aim we have proposed two approaches: common effect and maximal effect. The first, common effect approach, calculates total effect of all class-centers over searching point. Also, this approach is basis for mapping of novel method. The second, maximal effect approach, determines class-centers with the strongest effect over searching point.