A coevolutionary genetic algorithm using fuzzy clustering

Imrani, A. El; Bouroumi, Abdelaziz; Limouri, M.

doi:10.3233/ida-2000-43-402

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…The more the images are different, the greater would be the value of the result. By the similarity measurement shown in (5), the difference measurement between two FCH index vectors could also be calculated as (6):…”

Section: Image Querymentioning

confidence: 99%

“…However, FCM is significant sensitive to a set of initial clusters, as aforementioned. Such researches as [4][5][6][7] proposed an integration of GA and FCM in order to cope with the problem of FCM initialization. GA is an ////// Start GA /////// Denote (l) as the iteration index for GA; l = 1…”

Section: The Integration Of Genetic Algorithm With Fuzzy C-means (Gfcm)mentioning

confidence: 99%

“…Improper randomly initialization would lead FCM to produce inappropriate output of clusters. In order to relieve such a difficulty, most of the researches such as [4][5][6][7] proposed an integration between genetic algorithm (GA) [8,9] and the FCM for clustering purpose. Such an integration method is referred here as GFCM.…”

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confidence: 99%

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Modified fuzzy ants clustering approach

Supratid

Kim

2008

Appl Intell

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Being trapped in local optima within clustering search space currently is nontrivial difficulty. In order to relieve such a difficulty, even using genetic algorithm to optimize the initial clusters for fuzzy c-means is still unsatisfied. Since genetic algorithm intensifies only the current best solution, it will easily gets trapped in local minima. The ant colony system, dissimilarly to genetic algorithm, recognizes that the solutions near the best solution are also good ones and they bring about smoothness of solution. This paper proposes a modified fuzzy ant clustering. Such a presented method is a combination of genetic algorithm, ant colony system and fuzzy c-means. It is employed in creating fuzzy color histogram in image retrieval application. The performance measurement relates to the percentages of accuracy of image retrieval. Experimental results show that the proposed approach yields the best results among others with respect to sensitivity and robustness on dealing with lighting intensity changes, quantization errors, also changes in number of images and in size of color space, even the certainrange variation of a particular parameter of clustering.

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Section: Image Querymentioning

confidence: 99%

Section: The Integration Of Genetic Algorithm With Fuzzy C-means (Gfcm)mentioning

confidence: 99%

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Modified fuzzy ants clustering approach

Supratid

Kim

2008

Appl Intell

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Using genetic algorithm to support clustering-based portfolio optimization by investor information

Cheong

Kim

Byun

et al. 2017

Applied Soft Computing

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An Improved Robust Fuzzy Algorithm for Unsupervised Learning

Dik

Jebari

Ettouhami

2018

Journal of Intelligent Systems

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AbstractThis paper presents a robust, dynamic, and unsupervised fuzzy learning algorithm (RDUFL) that aims to cluster a set of data samples with the ability to detect outliers and assign the numbers of clusters automatically. It consists of three main stages. The first (1) stage is a pre-processing method in which possible outliers are determined and quarantined using a concept of proximity degree. The second (2) stage is a learning method, which consists in auto-detecting the number of classes with their prototypes for a dynamic threshold. This threshold is automatically determined based on the similarity among the detected prototypes that are updated at the exploration of a new data. The last (3) stage treats quarantined samples detected from the first stage to determine whether they belong to some class defined in the second phase. The effectiveness of this method is assessed on eight real medical benchmark datasets in comparison to known unsupervised learning methods, namely, the fuzzy c-means (FCM), possibilistic c-means (PCM), and noise clustering (NC). The obtained accuracy of our scheme is very promising for unsupervised learning problems.

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A coevolutionary genetic algorithm using fuzzy clustering

Cited by 4 publications

References 12 publications

Modified fuzzy ants clustering approach

Modified fuzzy ants clustering approach

Using genetic algorithm to support clustering-based portfolio optimization by investor information

An Improved Robust Fuzzy Algorithm for Unsupervised Learning

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