Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/2005
Title: Clustering Quality Improvement of k-means using a Hybrid Evolutionary Model
Authors: Karimov, Jeyhun
Özbayoğlu, Ahmet Murat
142991
Keywords: clustering
k-means
cluster-centroids
PSO
Simulated Annealing
Scatter Search
hybrid model
data mining
Issue Date: 2015
Publisher: ELSEVIER Science BV
Source: Karimov, J., & Ozbayoglu, M. (2015). Clustering quality improvement of k-means using a hybrid evolutionary model. Procedia Computer Science, 61, 38-45.
Abstract: Choosing good candidates for the initial centroid selection process for compact clustering algorithms, such as k-means, is essential for clustering quality and performance. In this study, a novel hybrid evolutionary model for k-means clustering (HE-kmeans) is proposed. This model uses meta-heuristic methods to identify the "good candidates" for initial centroid selection in k-means clustering method. The results indicate that the clustering quality is improved by approximately 30% compared to the standard random selection of initial centroids. We also experimentally compare our method with the other heuristics proposed for initial centroid selection and the experimental results show that our method performs better in most cases. (C) 2015 The Authors. Published by Elsevier B.V.
Description: Complex Adaptive Systems (2015 : San Jose; United States)
URI: https://www.sciencedirect.com/science/article/pii/S1877050915029737?via%3Dihub
https://hdl.handle.net/20.500.11851/2005
ISSN: 1877-0509
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Files in This Item:
File Description SizeFormat 
ozbayoglu-Clustering.pdf571.86 kBAdobe PDFThumbnail
View/Open
Show full item record

CORE Recommender

SCOPUSTM   
Citations

13
checked on Sep 23, 2022

WEB OF SCIENCETM
Citations

10
checked on Sep 24, 2022

Page view(s)

32
checked on Dec 5, 2022

Download(s)

8
checked on Dec 5, 2022

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.