A novel clustering approach: Artificial Bee Colony (ABC) algorithm

被引:731
作者
Karaboga, Dervis [1 ]
Ozturk, Celal [1 ]
机构
[1] Erciyes Univ, Intelligent Syst Res Grp, Dept Comp Engn, Kayseri, Turkey
关键词
Classification; Clustering analysis; Artificial Bee Colony algorithm; Particle Swarm Optimization; PARTICLE SWARM OPTIMIZATION; NEURAL-NETWORKS; MULTIVARIATE DATA; CLASSIFICATION;
D O I
10.1016/j.asoc.2009.12.025
中图分类号
TP18 [人工智能理论];
学科分类号
140502 [人工智能];
摘要
Artificial Bee Colony (ABC) algorithm which is one of the most recently introduced optimization algorithms, simulates the intelligent foraging behavior of a honey bee swarm. Clustering analysis, used in many disciplines and applications, is an important tool and a descriptive task seeking to identify homogeneous groups of objects based on the values of their attributes. In this work, ABC is used for data clustering on benchmark problems and the performance of ABC algorithm is compared with Particle Swarm Optimization (PSO) algorithm and other nine classification techniques from the literature. Thirteen of typical test data sets from the UCI Machine Learning Repository are used to demonstrate the results of the techniques. The simulation results indicate that ABC algorithm can efficiently be used for multivariate data clustering. (C) 2009 Elsevier B.V. All rights reserved.
引用
收藏
页码:652 / 657
页数:6
相关论文
共 42 条
[1]
[Anonymous], MATH CLASSIFICATION
[2]
[Anonymous], 1996, An introduction to Bayesian networks
[3]
[Anonymous], 2006, Swarm Intelligence in Data Mining
[4]
Basturk B., 2006, P IEEE SWARM INT S I
[5]
Blake CL, 1998, U CALIFORNIA IRVINE
[6]
Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[7]
Cleary J.G., 1995, PROC 12 INT C MACHIN, P108
[8]
Compton P., 1988, LECT NOTES ARTIF INT, P292
[9]
Facing classification problems with Particle Swarm Optimization [J].
De Falco, I. ;
Della Cioppa, A. ;
Tarantino, E. .
APPLIED SOFT COMPUTING, 2007, 7 (03) :652-658
[10]
DEMIROZ G, 1997, P 9 EUR C MACH LEARN, P85