The Adaptation of Gray Wolf Optimizer to Data Clustering

Year 2022, Volume: 25 Issue: 4, 1761 - 1767, 16.12.2022

Adem Tekerek , Murat Dörterler

https://doi.org/10.2339/politeknik.778630

Cited By: 4

Abstract

Data Clustering stands for a group of methods classifying patterns into groups and retrieving similarities or dissimilarities of a collection of objects. Clustering is used for pattern recognition, machine learning, etc. One of the approaches to clustering is optimization. The aim of the optimization is finding the best solution in the search space of a problem as much as possible. Many optimization methods were modified to solve clustering problems in literature. Gray Wolf Optimizer (GWO) is one of the nature-inspired meta-heuristic algorithms simulating the hunting of gray wolves. GWO has applied to solve several optimization issues in different fields. In this study, GWO was examined in the case of data clustering. GWO was modified to get better clustering results and applied to well-known benchmark data sets. The performance of GWO was compared to the other algorithms used as clustering. The results show that GWO can be used for data clustering successfully.

Keywords

Data clustering, meta-heuristic optimization, gray wolf optimizer, data mining

Bozkurt Optimizasyon Yönteminin Veri Kümelemeye Uyarlanması

Abstract

Veri Kümeleme, veri desenlerini gruplar halinde sınıflandıran ve bir nesne benzerliklerini veya farklılıklarını ayrıştıran bir yöntemlerdir. Kümeleme, örüntü tanıma, makine öğrenimi vb. için kullanılır. Veri Kümelemeye yönelik yaklaşımlardan biri de optimizasyondur. Optimizasyonun amacı, bir problemin arama alanında mümkün olan en iyi çözümün bulunmasıdır. Literatürdeki kümeleme problemlerini çözmek için birçok optimizasyon yöntemi uyarlanmıştır. Bozkurt Optimizasyonu (BO), boz kurtların avlanmasını simüle eden doğadan ilham alan sezgi ötesi algoritmalardan biridir. BO, farklı alanlardaki çeşitli optimizasyon sorunlarına başarılı çözüm üretmektedir. Bu çalışmada BO, veri kümeleme için incelenmiştir. BO, daha iyi kümeleme sonuçları elde etmek için değiştirilerek, iyi bilinen veri kümelerine kıyaslama amacıyla uygulanmıştır. BO'nun performansı, kümeleme olarak kullanılan diğer algoritmalarla karşılaştırılmıştır. Sonuçlar, BO'nun veri kümeleme için başarıyla kullanılabileceğini göstermektedir.

Keywords

Veri kümeleme, meta-sezgisel optimizasyon, bozkurt kurt optimizasyonu, veri madenciliği

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