A Fixed Suppressed Rate Selection Method for Suppressed Fuzzy C-Means Clustering Algorithm

Fan, Jiulun; Li, Jing

doi:10.4236/am.2014.58119

Cited by 9 publications

(5 citation statements)

References 17 publications

(12 reference statements)

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“…(3) and before starting the partition suppression using Eq. (10). The authors found their method successful in image segmentation problems, despite this entropy based suppression rate fully suppresses the crisp partition and applies no change in the completely ambiguous situation described by u ik = 1/c ∀i = 1, 2, .…”

Section: Time Variant Suppression Rate Based On Partition Entropymentioning

confidence: 98%

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A review on suppressed fuzzy c-means clustering models

Szilágyi

Lefkovits

Iclănzan

2020

Acta Universitatis Sapientiae, Informatica

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Suppressed fuzzy c-means clustering was proposed as an attempt to combine the better properties of hard and fuzzy c-means clustering, namely the quicker convergence of the former and the finer partition quality of the latter. In the meantime, it became much more than that. Its competitive behavior was revealed, based on which it received two generalization schemes. It was found a close relative of the so-called fuzzy c-means algorithm with generalized improved partition, which could improve its popularity due to the existence of an objective function it optimizes. Using certain suppression rules, it was found more accurate and efficient than the conventional fuzzy c-means in several, mostly image processing applications. This paper reviews the most relevant extensions and generalizations added to the theory of fuzzy c-means clustering models with suppressed partitions, and summarizes the practical advances these algorithms can offer.

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Section: Time Variant Suppression Rate Based On Partition Entropymentioning

confidence: 98%

“…Fan et al [10] proposed a constant suppression rate based on the distribution of the input data, defined as…”

Section: Constant Suppression Rate Based On Input Datamentioning

confidence: 99%

A review on suppressed fuzzy c-means clustering models

Szilágyi

Lefkovits

Iclănzan

2020

Acta Universitatis Sapientiae, Informatica

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“…In literature, various clustering algorithm was proposed to deal with clustering problem [11]- [14]. Subtractive, C-means and K-means are among the most commonly-used clustering algorithm…”

Section: The Clustering Algorithmsmentioning

confidence: 99%

“…For clustering, many algorithms are adopted in literature [11]- [14], in this paper we will focus on three fuzzy clustering algorithms that are subtractive, C-means and K-means clustering algorithms. The subtractive algorithm is used to determine the cluster number, whereas the C-means and the K-means will be exploited to generate the cluster centers then to construct the clusters.…”

Section: Introductionmentioning

confidence: 99%

Induction Motor Modeling Based on a Fuzzy Clustering Multi-Model—A Real-Time Validation

Abid¹,

Mouna²,

Sbita³

2015

IJMNTA

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This paper discusses a comparative study of two modeling methods based on multimodel approach. The first is based on C-means clustering algorithm and the second is based on K-means clustering algorithm. The two methods are experimentally applied to an induction motor. The multimodel modeling consists in representing the IM through a finite number of local models. This number of models has to be initially fixed, for which a subtractive clustering is necessary. Then both C-means and K-means clustering are exploited to determine the clusters. These clusters will be then exploited on the basis of structural and parametric identification to determine the local models that are combined, finally, to form the multimodel. The experimental study is based on MATLAB/SIMULINK environment and a DSpace scheme with DS1104 controller board. Experimental results approve that the multimodel based on K-means clustering algorithm is the most efficient.

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“…Then, in 2011 Beliakov G, Jams S introduced the fuzzy c-means clustering algorithm, it is one of dynamic clustering algorithm that minimize the error sum of squares of samples and the clustering center of a dynamic clustering algorithm [4]. It decides the samples belonging to which category type [5], the clustering algorithm cannot fully analyze the gray scale characteristics of the samples and the connection degree of adjacent pixels [6,7], but the fuzzy c-means clustering algorithm was effective to noise information. This paper introduces the theory of fuzzy c-mean clustering algorithm, and describes the marked watershed segmentation, then elaborates the improved algorithm.…”

Section: Introductionmentioning

confidence: 99%