| 基于数据场的CLARA聚类算法改进 |
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| 引用本文： | 郑涛,张帆.基于数据场的CLARA聚类算法改进[J].现代计算机,2006(6):19-21,36. |
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| 作者姓名： | 郑涛 张帆 |
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| 作者单位： | [1]中国民航飞行学院计算机学院,广汉618307 [2]西南大学信息学院,重庆400716 |
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| 摘 要： | CLARA是k-中心值聚类的一种算法,在处理大型数据集的聚类问题时,比PAM(围绕中心点的划分)更具有良好的伸缩性,但CLARA算法随机抽样中存在采样不准确的缺点.本文针对这一不足,使用了数据场的概念对CLARA聚类算法进行了有益的改进,提高了采样的准确性,使其更适合于对大型多维数据集的处理,提高了挖掘结果的质量.
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| 关 键 词： | 数据场 聚类 |
| 收稿时间： | 2006-03-22 |
| 修稿时间： | 2006-03-22 |
An CLARA Algorithm based on Data Field |
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| ZHENG Tao,ZHANG Fan.An CLARA Algorithm based on Data Field[J].Modem Computer,2006(6):19-21,36. |
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| Authors: | ZHENG Tao ZHANG Fan |
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| Affiliation: | 1. Computer College, Civil Aviation Flight University of China, Guanghan 618307; 2. Information College, Southwest University, Chongqing 400716 China |
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| Abstract: | CLARA is a K-Medoids algorithm. It is more efficient and flexible than PAM(Partitioning around Medoid) to handle large data sets. However, it is inaccurate in random sampling in algorithm CLARA. Based on data field conception, an improved CLARA is designed to solve the problem. This improved algorithm is more efficient and improves quality of result of data mining. |
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| Keywords: | CLARA |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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