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

Abstract

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

Automatic Generation Technology of Scrap Denim Clothes in Quilting Art Patterns by Integrating Computer Vision.

“…The garments in the recycled clothing box are first collected regularly by the management firm or individuals typically every six days [13]. These old clothes arrive at the collection company in 40 tons every day, and there are approximately 100 collection companies in Korea, resulting in more than 4,000 tons of clothing being dumped each day.…”

An improved hybrid defuzzification method for fuzzy controllers

“…In (9), the symbol ∪ represents the OR operator introduced between the rules. The symbol ∩ represents the AND operator used in the antecedent parts of the rules and × represents the THEN or fuzzy implication operator.…”

“…Aiming at improving the performance of fuzzy controllers, and providing systematic design procedures for the translation of the expert's knowledge in the form of fuzzy inference systems, various concepts have, so far, been developed. We state, for instance, the advent of the notion of self-organizing controllers [29,21], and the use of artificial neural networks and genetic algorithms in the design of adaptive fuzzy controllers [3,17,26] and others [2,9,24]. However, no performance enhancement nor systematic design technique has been sought, so far, by constructing a defuzzification method that integrates defuzzification into the overall setting of the controller components.…”