[1]胡爱策,任明仑,王浩.粒子群与细菌觅食相结合的案例聚类算法[J].计算机技术与发展,2013,(10):44-47.
　HU Ai-ce[],REN Ming-lun[],WANG Hao[].Case Clustering Algorithm Combining Particle Swarm Optimization and Bacterial Foraging[J].,2013,(10):44-47.
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粒子群与细菌觅食相结合的案例聚类算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2013年10期
页码:
44-47
栏目:
智能、算法、系统工程
出版日期:
1900-01-01

文章信息/Info

Title:
Case Clustering Algorithm Combining Particle Swarm Optimization and Bacterial Foraging
文章编号:
1673-629X（2013）10-0044-04
作者:
胡爱策1; 任明仑1; 2; 王浩1
[1]合肥工业大学 管理学院;[2]过程优化与智能决策教育部重点实验室
Author(s):
HU Ai-ce[1]; REN Ming-lun[1; 2]; WANG Hao[1]
关键词:
案例库; 粒子群算法; 细菌觅食算法; k-prototypes算法
Keywords:
case base; particle swarm optimization; bacterial foraging algorithm; k-prototypes algorithm
文献标志码:
A
摘要:
案例聚类是按照案例库中案例的相似度进行归类，目的是减少案例推理系统搜索相似案例的时间、提高案例推理系统的性能和降低案例库维护的复杂度。该问题的难度在于案例库的案例规模比较大和不同的聚类算法的选择对于聚类结果的影响。文中在粒子群算法与细菌觅食算法基础上，将两者结合起来，综合两个算法的优点，并将其应用在k-pro-totypes方法上对案例库中案例进行聚类。与流行的聚类算法进行比较，实验结果显示文中的算法具有更高的效率并且性能相对而言更加优秀
Abstract:
Case clustering is classified by the similarity to cases in case-base,the object is to reduce the time for searching similar case, improve the performance of case-base system and reduce the complexity of maintaining the case-base. The difficulty problem lies in that the size of case base is very large,and the clustering results is influenced by the choice of the clustering algorithm. In this paper,combined the advantages of particle swarm algorithm and bacterial foraging algorithm,use in case clustering with k-prototypes. Compared with pop-ular clustering algorithm show that this algorithm is efficient,has better performance

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更新日期/Last Update: 1900-01-01