| 基于信息熵的蚁群聚类算法的改进 |
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| 引用本文： | 赵伟丽,孙艳蕊,张志国,李金娜. 基于信息熵的蚁群聚类算法的改进[J]. 沈阳化工学院学报, 2005, 19(4): 296-300 |
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| 作者姓名： | 赵伟丽 孙艳蕊 张志国 李金娜 |
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| 作者单位： | 1. 东北大学理学院,辽宁,沈阳,110004
2. 沈阳化工学院,辽宁,沈阳,110142 |
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| 基金项目： | 国家博士后基金（No.2003033372） |
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| 摘 要： | 聚类分析是数据挖掘领域中的一个重要研究课题．在LF算法的基础上，利用信息熵减少参数设置，并通过半径递增、短期记忆、强行放下、合并聚类等策略，提高聚类性能、仿真实验表明：新算法能取得较好的聚类结果，对于处理混合属性数据集尤其是类属性数据集聚类问题相当有效．
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| 关 键 词： | 聚类分析 蚁群算法 信息熵 类属性 |
| 文章编号： | 1004-4639（2005）04-0296-05 |
| 收稿时间： | 2005-07-12 |
| 修稿时间： | 2005-07-12 |
An Improved Ant Colony Clustering Algorithm Based on Information Entropy |
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| ZHAO Wei-li,SUN YAN-rui,ZHANG Zhi-guo,LI Jin-na. An Improved Ant Colony Clustering Algorithm Based on Information Entropy[J]. Journal of Shenyang Institute of Chemical Technolgy, 2005, 19(4): 296-300 |
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| Authors: | ZHAO Wei-li SUN YAN-rui ZHANG Zhi-guo LI Jin-na |
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| Affiliation: | 1. Science College, Northeasters University, Shenyang 110004, China; 2. Shenyang Institute of Chemical Technology, Shenyang 110142, China |
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| Abstract: | Cluster analysis is a very important topic in the field of data mining. This paper, on the basis of LF algorithm, uses information entropy to reduce the number of parameters. By introducing some strategies such as radius increase, short-term memory, forced drop, cluster merge, etc., we make the performance improved. The experiment illustrates that our algorithm will obtain better clustering results and it is quite feasible for data sets with mixed attribute especially for categorical values. |
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| Keywords: | cluster analysis ant colony algorithm information entropy categorical attribute |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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