An improved approach to fuzzy clustering based on FCM algorithm and extended VIKOR method

Khanali, Hoda; Vaziri, Babak

doi:10.1007/s00521-019-04035-w

Cited by 8 publications

(2 citation statements)

References 17 publications

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“…T he number of correctly classif ied samples T he total number of samples (11) To consider have the correct assessment, the conditions for all experiments are similar to [7], and to compare the results of FVCM in [7] with experiment results of FVCM-FSVM are presented in tables. With regards to H. KHANALI, AND B. VAZIRI 625 [33], internal validation measures, namely Dunn's index, Davies-Bouldin index, and so on, achieve better results.…”

Section: Accuracy =mentioning

confidence: 99%

“…Fuzzy clustering has important applications in image processing, pattern recognition, object recognition, and so on [1,2,3] other similar techniques as fuzzy c-means [4], possibilistic fuzzy c-means (PFCM) [5], credibility fuzzy c-means (CFCM) [6]. FVCM [7] is an improved FCM algorithm to solve the problem of the sensitivity to noisy data in FCM. FVCM has a good performance in detecting noisy data.…”

Section: Introductionmentioning

confidence: 99%

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

Khanali

Vaziri

2021

Stat., optim. inf. comput.

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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.

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Section: Accuracy =mentioning

confidence: 99%