近似骨架导向的归约聚类算法

	近似骨架导向的归约聚类算法
	宗瑜 ; 李明楚 ; 江贺
刊名	电子与信息学报
	2009
卷号	31 期号:12 页码:2953-2957
关键词	聚类问题 NP-难解启发式算法近似骨架 Clustering issue NP-hard Heuristic algorithm Approximate backbone 骨架导向归约聚类算法 optimal solutions 近似骨架 search space 局部最优解 clustering algorithm Reduction Algorithm 聚类问题 high quality 算法收敛速度数据集研究成果搜索空间实验结果聚类质量基本思想骨架特征 multipleCluster analysis Computational complexity Feature extraction Heuristic algorithms Heuristic methods Optimal systems
ISSN号	1009-5896
其他题名	approximate backbone guided reduction algorithm for clustering
中文摘要	该文针对聚类问题上缺乏骨架研究成果的现状,分析了聚类问题的近似骨架特征,设计并实现了近似骨架导向的归约聚类算法.该算法的基本思想是:首先利用现有的启发式聚类算法得到同一聚类实例的多个局部最优解,通过对局部最优解求交得到近似骨架,将近似骨架固定得到规模更小的搜索空间,最后在新空间上求解.在26个仿真数据集和3个实际数据集上的实验结果表明,骨架理论对提高聚类质量、降低初始解影响及加快算法收敛速度等方面均十分有效.
语种	中文
公开日期	2011-03-18
附注	In this paper, the characteristic of approximate backbone is analyzed and an Approximate Backbone guided Reduction Algorithm for Clustering (ABRAC) is proposed. ABRAC works as follows: firstly, multiple local optimal solutions are obtained by an existing heuristic clustering algorithm; then, the approximate backbone is generated by intersection of local optimal solutions; afterwards, the search space can be dramatically reduced by fixing the approximate backbone; finally, this reduced search space can be efficiently searched to find high quality solutions. Extensively wide experiments on 26 synthetic and 3 real-life data sets demonstrate that the backbone has significantly effects for improving the quality of clustering, reducing the impact of initial solution, and speeding up the convergence rate.
内容类型	期刊论文
源URL	[http://ir.iscas.ac.cn/handle/311060/8084]
专题	软件研究所_计算机科学国家重点实验室 _期刊论文
推荐引用方式 GB/T 7714	宗瑜,李明楚,江贺. 近似骨架导向的归约聚类算法[J]. 电子与信息学报,2009,31(12):2953-2957.
APA	宗瑜,李明楚,&江贺.(2009).近似骨架导向的归约聚类算法.电子与信息学报,31(12),2953-2957.
MLA	宗瑜,et al."近似骨架导向的归约聚类算法".电子与信息学报 31.12(2009):2953-2957.