K-ORTALAMALAR TABANLI EN ETKİLİ META-SEZGİSEL KÜMELEME ALGORİTMASININ ARAŞTIRILMASI

Year 2020, , 173 - 184, 29.12.2020

Ömer Köroğlu , Hamdi Kahraman

https://doi.org/10.21923/jesd.828575

Cited By: 1

Abstract

Kümeleme uygulamalarında en sık kullanılan algoritmalardan biri olan k-ortalamalar yönteminin tatbik edilmesinde karşılaşılan başlıca zorluk, gözlem sayısına bağlı olarak hesaplama karmaşıklığının artması ve problem için küresel en iyi çözüme yakınsayamamadır. Üstelik problem boyutunun ve karmaşıklığının artması halinde k-ortalamalar yönteminin performansı daha da kötüleşmektedir. Tüm bu nedenlerden ötürü klasik k-ortalamalar prosedürü yerine daha hızlı ve başarılı bir kümeleme algoritması geliştirme çalışmaları önem kazanmaktadır. Meta-sezgisel kümeleme (MSK) algoritmaları bu amaçla geliştirilmişlerdir. MSK algoritmaları sahip oldukları arama yetenekleri sayesinde karmaşık kümeleme problemlerinde yerel çözüm tuzaklarından kurtulabilmekte ve küresel çözüme başarılı bir şekilde yakınsayabilmektedirler. Bu makale çalışmasında literatürde yer alan güncel ve güçlü meta-sezgisel arama (MSA) teknikleri kullanılarak MSK algoritmaları geliştirilmekte ve performansları karşılaştırılarak en etkili yöntem araştırılmaktadır. Bu amaçla güncel ve güçlü MSA teknikleri ile k-ortalamalar yöntemi melezlenerek 10 farklı MSK algoritması geliştirilmiştir. Geliştirilen algoritmaların performanslarını ölçmek için 5 farklı kümeleme veri seti kullanılmıştır. Deneysel çalışmalardan elde edilen veriler istatistiksel test yöntemleri kullanılarak analiz edilmiştir. Analiz sonuçları, makalede geliştirilen MSK algoritmaları arasında AGDE tabanlı yöntemin hem yakınsama hızı hem de küresel optimum çözüme yakınsama miktarı açısından kümeleme problemlerinde rakiplerine kıyasla üstün bir performansa sahip olduğunu göstermektedir.

Keywords

Kümeleme, K-Ortalamalar Yöntemi, Meta-Sezgisel Arama Algoritması, Meta-Sezgisel Kümeleme Algoritması

Supporting Institution

TÜBİTAK

Project Number

1919B011904077

Thanks

Bu çalışmada yürütülen faaliyetler, 2020 yılında TÜBİTAK 2209-A Üniversite Öğrencileri Yurt İçi Araştırma Projeleri Destek Programı kapsamında 1919B011904077 numaralı proje olarak TUBİTAK tarafından desteklenmiştir.

RESEARCH OF MOST EFFECTIVE K-MEANS BASED META HEURISTIC SEARCH ALGORITHM

Abstract

One of the most frequently used algorithms in clustering analysis, the main difficulty encountered in applying the k-means method is that the calculation complexity increases due to the number of observations and it cannot converge to the global best solution for the problem. Moreover, if the problem size and complexity increases, the performance of the k-means method gets worse. For all these reasons, it is important to develop a faster and successful clustering algorithm instead of the classical k-means procedure. Meta-heuristic clustering (MSK) algorithms have been developed for this purpose. Thanks to their search capabilities, MSK algorithms can get rid of local solution traps in complex clustering problems and successfully converge to the global solution. Therefore, the cluster success of MSK methods is directly affected by the search success of MSA techniques. In this article, MSK methods are developed by using current and powerful MSA techniques in the literature and the most effective method is investigated by comparing the performance of these algorithms. For this purpose, ten different MSK algorithms have been developed by hybridizing the k-means method with current and powerful MSA techniques. Five different clustering data sets were used to measure the performance of the developed algorithms. Data obtained from experimental studies were analyzed using statistical test methods. The results of the analysis show that among the MSK algorithms developed in the article, the AGDE-based method has a superior performance compared to its competitors in cluster problems in terms of both the convergence rate and the amount of convergence to the global optimum solution.

Keywords

Clustering, K-Means, Meta-Heuristic Search Algorithm, Meta-Heuristic Clustering Algorithm

Project Number

1919B011904077

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