Clustering Based on Fuzzy Adaptive Particle Swarm Optimization Approach

Year 2020, , 279 - 296, 15.01.2020

Mehmet Aksaraylı , Osman Pala

https://doi.org/10.17541/optimum.575499

Abstract

The process of grouping the observation points with the same characteristics in terms of the values of the characteristics is called clustering analysis. The aim of clustering, which is one of the techniques of machine learning, is to provide the implementation of different strategies for observation points in the different group by grouping the observation points. In clustering used in many disciplines, the process becomes difficult when there is no preliminary information such as the number of clusters. In cases where the number of sets is not predetermined, heuristic algorithms can be used, which can determine the number of sets according to the fitness function. In this study, in order to evaluate the success of the proposed Fuzzy Particle Swarm Optimization heuristic algorithm and fitness function in clustering, data sets frequently used in clustering analysis were utilized. According to the results of the analysis, it was observed that the proposed algorithm indicated a quite successful performance in finding the correct number of clusters and in the correct grouping of observations.

Keywords

Particle Swarm Optimization, Clustering Analysis, Fuzzy Adaptive

Bulanık Parçacık Sürü Optimizasyon Yaklaşımı Temelli Kümeleme

Abstract

Aynı özelliklere sahip gözlem noktalarını, özelliklerinin aldığı değerler açısından gruplara ayırma işlemine kümeleme analizi adı verilmektedir. Makine öğrenmesi tekniklerinden olan kümelemede amaç, gözlem noktalarını gruplayarak farklı gruptaki gözlem noktaları için farklı stratejiler uygulanmasının sağlanmasıdır. Birçok bilim dalında kullanılan kümelemede, küme sayısı gibi ön bilgilerin bulunmadığı durumlarda işlem zorlaşmaktadır. Küme sayısı önceden belirli olmadığı durumlarda, uygunluk fonksiyonuna göre küme sayısını belirleyebilen sezgisel algoritmalar kullanılabilmektedir. Çalışmada, önerilen Bulanık Parçacık Sürü Optimizasyonu sezgisel algoritmasının ve uygunluk fonksiyonunun kümelemedeki başarısını değerlendirebilmek adına kümeleme analizinde sıklıkla kullanılan veri setlerinden faydalanılmıştır. Analiz sonuçlarına göre önerilen algoritmanın doğru küme sayısını bulmada ve gözlemleri doğru gruplamada klasik yaklaşıma göre daha yüksek başarım gösterdiği gözlenmiştir.

Keywords

Parçacık Sürü Optimizasyonu, Kümeleme Analizi, Bulanık Adaptif

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