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Data Clustering Using the Cooperative Search Based Artificial Bee Colony Algorithm
Chen Guo1; Heng Tang1; Chang Boon Patrick Lee1; Ben Niu2
2019
Conference NameInternational Conference on Intelligent Computing
Source PublicationICIC 2019: Intelligent Computing Methodologies
Pages660-671
Conference DateAugust 3–6, 2019
Conference PlaceNanchang, China
Abstract

Data clustering is a significant and strong data analysis technique which has a broad range of applications in many domains. In this paper, the artificial bee colony algorithm (ABC) is adopted to partition data sets into K clusters. To trade off the global and local searching ability of ABC algorithm, two kinds of cooperative search based ABC algorithms are proposed, that is N2ABC and WCABC. Then, the proposed algorithms are combined with K-means to deal with data clustering. For the purpose of demonstrating the efficiency of two hybrid clustering algorithms (N2ABCC and WCABCC), one artificial data set and six benchmark data sets are selected to test clustering results. Meanwhile, five algorithms, namely K-means, PSOC, ABCC, GABCC and CABCC, are chosen for comparison. The clustering results indicate that the proposed algorithms have better clustering validity than other algorithms.

KeywordData Clustering K-means Artificial Bee Colony Algorithm
DOIhttps://doi.org/10.1007/978-3-030-26766-7_60
Language英语
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Document TypeConference paper
CollectionFaculty of Business Administration
DEPARTMENT OF ACCOUNTING AND INFORMATION MANAGEMENT
Affiliation1.Faculty of Business Administration University of Macau MacauChina
2.College of Management Shenzhen University Shenzhen China
First Author AffilicationFaculty of Business Administration
Recommended Citation
GB/T 7714
Chen Guo,Heng Tang,Chang Boon Patrick Lee,et al. Data Clustering Using the Cooperative Search Based Artificial Bee Colony Algorithm[C],2019:660-671.
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