Interval type-2 credibilistic clustering for pattern recognition

Kalhori, M. Rostam Niakan; Zarandi, M. H. Fazel

doi:10.1016/j.patcog.2015.04.010

Cited by 19 publications

(3 citation statements)

References 49 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%

“…FVCM improves the accuracy and computational speed. The sensitivity to noisy and outlier data is one of FCM problems that a robust clustering approach called TCLUST [8] , fuzzy c-means-relaxed constraints support vector machine (FCM-RSVM) [9] , Relative entropy fuzzy c-means (REFCM) [10], and algorithms based on type-2 fuzzy sets such as [11,12] have recently been proposed to solve this problem. In addition, a comparison of partition algorithms is presented by Khanali and Vaziri in [13] .…”

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%

Section: Introductionmentioning

confidence: 99%

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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“…Multi-center Fuzzy C-means algorithm based on Transitive Closure and Spectral Clustering (MFCM-TCSC) [27] uses the multi-center initialization method to solve sensitive problems to initialize for FCM algorithm, and applies non-traditional curved clusters. To ensure the extraction of spectral features, Floyd algorithm provides a similarity Matrix used block symmetric.…”

Section: Improved Partition Algorithmsmentioning

confidence: 99%

A Survey on Clustering Algorithms for Partitioning Method

Khanali¹,

Vaziri²

2016

IJCA

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Clustering is one of the data mining methods. In all clustering algorithms, the goal is to minimize intracluster distances, and to maximize intercluster distances. Whatever a clustering algorithm provides a better performance, it has the more successful to achieve this goal. Nowadays, although many research done in the field of clustering algorithms, these algorithms have the challenges such as processing time, scalability, accuracy, etc. Comparing various methods of the clustering, the contributions of the recent researches focused on solving the clustering challenges of the partition method. In this paper, the partitioning clustering method is introduced, the procedure of the clustering algorithms is described, and finally the new improved methods and the proposed solutions to solve these challenges are explained.

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