| 引用本文: | 巩敦卫,蒋余庆,张 勇,周 勇.基于微粒群优化聚类数目的K–均值算法[J].控制理论与应用,2009,26(10):1175~1179.[点击复制] |
| GONG Dun-wei,JIANG Yu-qing,ZHANG Yong,ZHOU Yong.K-mean algorithm for optimizing the number of clusters based on particle swarm optimization[J].Control Theory and Technology,2009,26(10):1175~1179.[点击复制] |
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| 基于微粒群优化聚类数目的K–均值算法 |
| K-mean algorithm for optimizing the number of clusters based on particle swarm optimization |
| 摘要点击 1923 全文点击 1418 投稿时间：2007-10-22 修订日期：2009-01-04 |
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| DOI编号 10.7641/j.issn.1000-8152.2009.10.023 |
| 2009,26(10):1175-1179 |
| 中文关键词 聚类 K–均值算法 微粒群优化 微粒更新 |
| 英文关键词 clustering K-means algorithm particle swarm optimization particle update |
| 基金项目 江苏省自然科学基金资助项目(BK2008125); 教育部新世纪优秀人才支持计划资助项目(NCET–07–0802). |
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| 中文摘要 |
| K–均值算法是广泛使用的聚类算法, 但该算法的聚类数目难以确定, 且聚类结果对初始聚类中心比较敏感. 本文提出一种基于微粒群优化聚类数目的K–均值算法, 该算法采用聚类中心的坐标和通配符表示微粒位置, 通过定义微粒更新公式中新的加减运算符, 动态调整聚类中心的数目及坐标, 此外, 以改进的聚类有效性指标Davies-Bouldin准则作为适应度函数. 5个人工和真实数据集的聚类结果验证了所提算法的优越性. |
| 英文摘要 |
| K-mean algorithm is a widely used clustering method, but it is difficult to determine the number of clusters; and the clustering result is sensitive to initial cluster centers. We present a novel K-mean algorithm for optimizing the number of clusters based on particle swarm optimization. The algorithm denotes the position of a particle with the coordinates
of cluster centers and wildcards. The coordinates of cluster centers are dynamically djusted by defining the new plus and new minus operators in the particle update formula. In addition, an improved Davies-Bouldin index is employed to evaluate the efficiency of a clustering result. Experimental results of 5 sets of artificial and real-world data validate the advantages of the proposed algorithm. |
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