A Review on Artificial Bee Colony Algorithms and Their Applications to Data Clustering

Data clustering is an important data mining technique being widely used in numerous applications. It is a method of creating groups (clusters) of objects, in such a way that objects in one cluster are very similar and objects in different clusters are quite distinct, i.e. intra-cluster distance is m...

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Bibliographic Details
Published inCybernetics and information technologies : CIT Vol. 17; no. 3; pp. 3 - 28
Main Authors Kumar, Ajit, Kumar, Dharmender, Jarial, S. K.
Format Journal Article
LanguageEnglish
Published De Gruyter Open 01.09.2017
Sciendo
Subjects
artificial bee colony
Data clustering
meta-heuristics
optimization
swarm intelligence
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Summary:Data clustering is an important data mining technique being widely used in numerous applications. It is a method of creating groups (clusters) of objects, in such a way that objects in one cluster are very similar and objects in different clusters are quite distinct, i.e. intra-cluster distance is minimized and inter-cluster distance is maximized. However, the popular conventional clustering algorithms have shortcomings such as dependency on center initialization, slow convergence rate, local optima trap, etc. Artificial Bee Colony (ABC) algorithm is one of the popular swarm based algorithm inspired by intelligent foraging behaviour of honeybees that helps to minimize these shortcomings. In the past, many swarm intelligence based techniques for clustering were introduced and proved their performance. This paper provides a literature survey on ABC, its variants and its applications in data clustering.
ISSN:1314-4081
1314-4081
DOI:10.1515/cait-2017-0027