A new credibilistic clustering algorithm

Kalhori, M. Rostam Niakan; Zarandi, M. H. Fazel; Türkşen, I.B.

doi:10.1016/j.ins.2014.03.106

Cited by 9 publications

(4 citation statements)

References 42 publications

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“…In [5] credibility measure instead of possibility measure and used Mahalanobis distance to introduce a new clustering method. The goals of model are to minimize average of cluster centers error and separating clusters from each other.…”

Section: Review Of Literaturementioning

confidence: 99%

A New Credibilistic Clustering Method with Mahalanobis Distance

Rafati¹,

Researchers²,

Akbarpour³

2018

IJMSC

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The fuzzy c-means (FCM) is the best known clustering and use the degree of membership fuzzy to data clustering. But the membership is not always for all data correctly. That is, at scattered dataset belonging is less and noisy dataset belonging is more assigned and local optimization problem occurs. Possibility c-means (PCM) was introduced to correspond weaknesses FCM approach. In PCM was not self-duality property. In other words, a sample membership for all clusters be assigned more than one and basic condition FCM were violated. One of the new method is Credibilistic clustering and based on credibility theory proposed that is used to study the behavior of fuzzy phenomenon. The aim is to provide new Credibilistic clustering approach with replacing credibility measure instead of the fuzzy membership and Mahalanobis distance use in FCM objective function. Credibility measure has self-duality property and solves coincident clustering problem. Mahalanobis distance used instead of Euclidian distance to separate cluster centers from each other and dens samples of each cluster. The result of proposed method is evaluated with three numeric dataset and Iris dataset. The most important challenge will be how to choose the initial cluster centers in the noisy dataset. In the future, we can be used FCM combined with particle swarm optimization.

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Section: Review Of Literaturementioning

confidence: 99%

A New Credibilistic Clustering Method with Mahalanobis Distance

Rafati¹,

Researchers²,

Akbarpour³

2018

IJMSC

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show abstract

“…A modified SCCPSO was presented by discussing the membership functions and designing a pre-selection method in [14] by Wen et al The analysis of computational complexity demonstrated the feasibility of the modified SCCPSO. Niakan et al in [15] a new credibility-based objective function has been defined for type-1 fuzzy clustering. The uncertainty in parameters and distance measure has not been considered in [15].…”

Section: Definition 1 Credibility Measurementioning

confidence: 99%

“…Niakan et al in [15] a new credibility-based objective function has been defined for type-1 fuzzy clustering. The uncertainty in parameters and distance measure has not been considered in [15].…”

Section: Definition 1 Credibility Measurementioning

confidence: 99%

Interval type-2 credibilistic clustering for pattern recognition

Kalhori

Zarandi

2015

Pattern Recognition

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“…Aforementioned affinity is desired due to the deep mathematical background of probability theory. Credibility theory has been improved by Love, Guo, and Li (2007) by adding hazard functions and even a clustering algorithm has been proposed by Rostam Niakan Kalhori, Fazel Zarandi, and Turksen (2014).…”

Section: Introductionmentioning

confidence: 99%