Similarity Technique Effectiveness of Optimized Fuzzy C-means Clustering Based on Fuzzy Support Vector Machine for Noisy Data

Fuzzy VIKOR C-means (FVCM) is a kind of unsupervised fuzzy clustering algorithm that improves the accuracyand computational speed (FCM). So it reduces sensitivity to noisy and outlier data, enhances performance quality clusters. Since FVCM allocates some data specific cluster based on similarity technique, reducing effect increases This paper presents new approach accurate location clusters overcoming constraints points through support vector machine (FSVM), called FVCM-FSVM, so at each stage samples with high degree membership are selected for training in classification FSVM. Then, labels remaining predicted process continues until convergence FVCM-FSVM. The results numerical experiments showed proposed has better than FVCM. Of course, greatly achieves accuracy.