New initialization approaches for the k-means and particle swarm optimization based clustering algorithms
Küçük Resim Yok
Tarih
2018
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Gazi Univ, Fac Engineering Architecture
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Thanks to the recent advances in microarray technology, simultaneously expressing different levels of genes is possible. Although the representation of confidential information in genes simplifies to analyze them; both high number of genes and high amount of noise in the data sets make difficult to identify the gene data. In order to identify genes various clustering methods are generally used. Microarray data is one of the best examples of multidimensional data. In this study, in order to cluster multidimensional data, new methods for selecting initial cluster centers are proposed for the standard K-means and Particle Swarm Optimization (PSO)-based clustering algorithms. Also, coreset approach is adapted for PSO algorithm. The correctness of the developed methods is examined on datasets which are frequently used in the literature, and also these proposed approaches are run on Colon Cancer microarray data set. The performance of the proposed approaches is compared with the standard K-means and PSO-based clustering methods by means of average iteration number, Rand, and Silhouette index metrics. In experimental studies, we observe that proposed methods give superior results on the normalized datasets in which feature selection process is performed.
Açıklama
Anahtar Kelimeler
Clustering, particle swarm optimization, k-means, initial centroid selection, coreset
Kaynak
Journal of the Faculty of Engineering and Architecture of Gazi University
WoS Q Değeri
Q4
Scopus Q Değeri
Cilt
33
Sayı
2
