The proposal of a fuzzy clustering algorithm based on particle swarm

Szabó, Adrienn; Castro, Leandro Nunes de; Delgado, Myriam Regattieri

doi:10.1109/nabic.2011.6089630

Cited by 17 publications

(6 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Perbedaan struktural utama antara PSO dan PSC adalah [3] :  Setiap partikel dalam PSO merupakan solusi potensial untuk masalah ini. Pada PSC, setiap partikel merupakan solusi dalam pengelompokkan data.…”

Section: B K-meansunclassified

Perbandingan Rapid Centroid Estimation (RCE) — K Nearest Neighbor (K-NN) Dengan K Means — K Nearest Neighbor (K-NN)

2017

View full text Add to dashboard Cite

Teknik Clustering terbukti dapat meningkatkan akurasi dalam melakukan klasifikasi, terutama pada algoritma K-Nearest Neighbor (K-NN). Setiap data dari setiap kelas akan membentuk K cluster yang kemudian nilai centroid akhir dari setiap cluster pada setiap kelas data tersebut akan dijadikan data acuan untuk melakukan proses klasifikasi menggunakan algoritma K-NN. Namun kendala dari banyaknya teknik clustering adalah biaya komputasi yang mahal, Rapid Centroid Estimation (RCE) dan K-Means termasuk kedalam teknik clustering dengan biaya komputasi yang murah. Untuk melihat manakah dari kedua algoritma ini (RCE dan K-Means) yang lebih baik memberikan peningkatan akurasi pada algoritma K-NN maka, pada penelitian ini akan mencoba untuk membandingkan kedua algoritma tersebut. Hasil dari penelitian ini adalah gabungan RCE—K-NN memberikan hasil akurasi yang lebih baik dari K-Means—K-NN pada data set iris dan wine. Namun dalam perubahan nilai akurasi RCE—K-NN lebih stabil hanya pada data set iris. Sedangkan pada data set wine, K-Means—K-NN terlihat mendapati perubahan akurasi yang lebih stabil dibandingkan RCE—K-NN.

show abstract

Section: B K-meansunclassified

Perbandingan Rapid Centroid Estimation (RCE) — K Nearest Neighbor (K-NN) Dengan K Means — K Nearest Neighbor (K-NN)

2017

View full text Add to dashboard Cite

show abstract

“…To evaluate the performance of the proposed algorithm, the FCM [17], FPSC [15], FaiNet [16], FcPSC and cPSC [9] algorithms were implemented in Matlab ® and applied to six datasets from the UCI Machine Learning Repository (http://archive.ics.uci.edu/ml/datasets.html): Ecoli, Iris, Pima Indians Diabetes, Yeast, Ruspini and Glass Identification. For the FCM and FPSC algorithms, the number of prototypes used was equal to the number of classes present in the respective databases.…”

Section: Performance Assessmentmentioning

confidence: 99%

“…The inertia moment (ω) has an initial value of 0.90, with an iterative decay of 95% at each iteration until the value 0.01 is obtained. The position and velocity of the particles are also controlled, ranging from [0,1] to [−0.1,0.1], respectively [15]. In FaiNet, the values of (death threshold) and (suppression threshold) are set equal to 0.5 [16].…”

Section: Performance Assessmentmentioning

confidence: 99%

See 1 more Smart Citation

A Constructive Particle Swarm Algorithm for Fuzzy Clustering

Szabó

Castro

Delgado

2012

Intelligent Data Engineering and Automated Learning - IDEAL 2012

Self Cite

View full text Add to dashboard Cite

Abstract. This paper proposes a fuzzy version of the crisp cPSC (Constructive Particle Swarm Clustering), called FcPSC (Fuzzy Constructive Particle Swarm Clustering). In addition to detecting fuzzy clusters, the proposed algorithm dynamically determines a suitable number of clusters in the datasets without the need of prior knowledge, necessary in cPSC to control the number of particles in the swarm. The FcPSC algorithm was applied to six databases from the literature and its performance was compared with that of Fuzzy C-Means, a Fuzzy Artificial Immune Network, a Fuzzy Particle Swarm Clustering and the crisp cPSC. FcPSC showed to be competitive with the algorithms used for comparison and the number of particles generated was smaller than for cPSC.

show abstract

“…Within this issue, several population based algorithms have been proposed like genetic algorith m (GA) [13,14], Ant colony Optimization (ACO) [15,16], Particle Swarm Optimization (PSO) [17,18,19,20,21], Differential Evolution (DE) [22,23,24,25,26], Artificial Bees Colony (ABC) [27,28] among others.…”

Section: Introductionmentioning

confidence: 99%

Handling Fuzzy Image Clustering with a Modified ABC Algorithm

Ouadfel¹,

Meshoul²

2012

IJISA

View full text Add to dashboard Cite

Abstract-Image segmentation can be cast as a clustering task where the image is partitioned into clusters. Pixels within the same cluster are as homogenous as possible whereas pixels belonging to different clusters are not similar in terms of an appropriate similarity measure. Several clustering methods have been proposed for image seg mentation purpose among wh ich the Fu zzy C-Means clustering algorith m. However this algorith m still suffers fro m some drawbacks, such as local optima and sensitivity to init ialization. Art ificial Bees Co lony algorith m is a recent population-based optimization method which has been successfully used in many complex problems. In this paper, we propose a new fuzzy clustering algorithm based on a modified Artificial Bees Colony algorith m, in which a new mutation strategy inspired from the Differential Evolution is introduced in order to improv e the explo itation process. Experimental results show that our proposed approach imp roves the performance o f the basic fuzzy C-Means clustering algorith m and outperforms other population based optimization methods.

show abstract

The proposal of a fuzzy clustering algorithm based on particle swarm

Cited by 17 publications

References 20 publications

Perbandingan Rapid Centroid Estimation (RCE) — K Nearest Neighbor (K-NN) Dengan K Means — K Nearest Neighbor (K-NN)

Perbandingan Rapid Centroid Estimation (RCE) — K Nearest Neighbor (K-NN) Dengan K Means — K Nearest Neighbor (K-NN)

A Constructive Particle Swarm Algorithm for Fuzzy Clustering

Handling Fuzzy Image Clustering with a Modified ABC Algorithm

Contact Info

Product

Resources

About