Applying Honey-Bee Mating Optimization and Particle Swarm Optimization for Clustering Problems

應用蜜蜂繁殖演算法與粒子群演算法於分群問題

邱垂昱(Chui-Yu Chiu) ； 郭宜婷(I-Ting Kuo)

《工業工程學刊》 26卷5期 (2009/09) Pp. 426-431

https://doi.org/10.29977/JCIIE.200909.0010

摘要

隨著資訊技術的成熟以及資料庫技術的穩健發展，各項資料的取得益加便捷，資料庫規模日漸龐大，如何有效的運用分析這些資料，找出隱含其中的重要資訊，是近年來頗受重視的研究方向。而最被廣爲討論的分析技術即爲分群。分資料分群的目的是在於將資料集切割爲數個群集，使得這些群集內的各筆資料間的相似度(intra-cluster)爲最高，而位於不同群集的資料間(inter-cluster)的相似度爲最低；部分的學者在描述群集時，會根據群集內部的同質性(homogeneity)以及外部的分離性(separation)作爲衡量指標，使資料在相同的群集中彼此應該很相似，而在不同群集應該要有所差異。本研究擬結合粒子群最佳化方法與蜜蜂繁殖演算法，兩種啟發式演算法發展出一套群集分析方法，進行有效的探勘與分析程序，建構一套快速精準的演算法，以利未來更廣泛的應用。

關鍵字

分群 ； 粒子群演算法 ； 蜜蜂繁殖演算法

並列摘要

The use of information technologies in the various business areas is emerging in recent years. Mining the useful information existed in vast data has become an important issue. Clustering analysis which tries to segment data into homogeneous clusters is one of the most useful technologies in data mining methods. In this study, we proposed a clustering method which integrates particle swarm optimization with honey-bee mating optimization. Simulations for three benchmark test functions (MSE, intra-cluster distance and inter- cluster distance) are performed. According to the lowest MSE and the intra-cluster distance/ inter-cluster distance value, experiment results show that our proposed method possesses better ability to find the global optimum than other well-known clustering algorithms.

並列關鍵字

clustering ； honey-bee optimization ； particle swarm optimization

參考文獻

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