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一种多空间聚类算法 被引量：6

A Multi-Space Clustering Algorithm
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摘要 CLARANS算法是经典的划分聚类算法,其核心思想是采用随机重启的局部搜索方式搜索中心点.由于搜索空间布满了局部最优解的“陷阱”,因此它难以获得全局最优解,从而影响了聚类质量.针对这个缺点,本文将多空间思想与CLARANS算法相结合,提出了基于多空间思想的CLARANS算法—CABMS(CLARANSAlgorithmBasedonMulti-Space).该算法的基本思路是采用空间变换策略构造一系列光滑程度不同的搜索空间,在不同的搜索空间中执行CLARANS算法,并利用前层搜索空间的聚类结果来引导本层搜索空间的聚类.CABMS能够跳过局部最优解的“陷阱”,增大获得全局最优解的概率,达到提高聚类质量的目的.本文给出了等距法多空间构造策略,并通过实验对比了CLARANS算法与CABMS算法的聚类质量.实验结果表明,CABMS的聚类质量较CLARANS有较大改进. As a classical partition clustering algorithm, CLARANS uses local search with random restart to find clusters＇ central points. Due to the great number of local optimum ＂traps＂, it is hard for CLARANS to find the global optimum solutions. This paper incorporates the Multi-Space theory into CLARANS, and proposes one new algorithm CLARANS Algorithm Based on Multi-Space （CABMS）. Its basic idea is to construct a series of smoothed search spaces with space transformation strategy, and runs CLARANS in every search space. CABMS uses the solution in the former search space as an initial solution for the current space. Through such way, CABMS could jump out of the local optimum ＂traps＂, and improve the probability of getting global optimum. This paper gives out Displacement strategy for space transformation, and experiment results indicated that the CABMS algorithm had achieved remarkable improvement over CLARANS in terms of quality.
作者 赵东东 宗瑜 江贺 张宪超
机构地区 大连理工大学软件学院
出处 《小型微型计算机系统》 CSCD 北大核心 2006年第12期2297-2300,共4页 Journal of Chinese Computer Systems
基金 国家自然科学基金项目(90412007)资助 国家自然科学基金项目(60503003)资助 辽宁省博士启动基金项目(20051082)资助 大连理工大学青年教师培养基金资助.
关键词 聚类 多空间 CLARANS clustering multi-space CLARANS
分类号 TP311 [自动化与计算机技术—计算机软件与理论]
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参考文献14

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二级参考文献36

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共引文献85

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小型微型计算机系统
2006年 第12期

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