A novel clustering approach: Artificial Bee Colony (ABC) algorithm
A novel clustering approach: Artificial Bee Colony (ABC) algorithm
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DOI：
10.1016/j.asoc.2009.12.025
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发表时间：
2011-01-01
影响因子：
8.7
通讯作者：
Ozturk, Celal
中科院分区：
文献类型：
--
作者：
Karaboga, Dervis;Ozturk, Celal
人工蜂群（Artificial Bee Colony， ABC）算法是一种模拟蜂群智能觅食行为的优化算法。聚类分析在许多学科和应用中都有使用，它是一种重要的工具，也是一项描述性任务，旨在根据对象的属性值来识别同质的对象组。本文将ABC算法用于基准问题的数据聚类，并将ABC算法的性能与粒子群优化算法（Particle Swarm Optimization， PSO）和文献中的其他九种分类技术进行了比较。来自UCI机器学习存储库的13个典型测试数据集用于演示技术的结果。仿真结果表明，ABC算法可以有效地用于多变量数据聚类。(C) 2009 Elsevier B.V.版权所有
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.