| 基于Web日志挖掘的Web文档聚类 |
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| 引用本文： | 高哲,魏海平,王福威,赵晓碧.基于Web日志挖掘的Web文档聚类[J].计算机工程与设计,2008,29(18). |
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| 作者姓名： | 高哲 魏海平 王福威 赵晓碧 |
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| 作者单位： | 辽宁石油化工大学,计算机与通信工程学院,辽宁,抚顺,113001 |
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| 摘 要： | Web日志挖掘是Web挖掘的一种,介绍了Web日志挖掘的一般过程,研究了k-means聚类算法,并分析了k-means聚类算法的不足.k-means聚类算法迭代过程中每次都需要计算每个数据对象到簇质心的距离,使得聚类效率不高,针对这个问题,提出了k-means聚类算法的改进算法,该算法避免了重复计算数据对象到簇质心的距离,并用这两种算法实现了Web文档的聚类.试验结果表明,该改进算法提高了聚类效率.
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| 关 键 词： | 日志挖掘 Web日志 文档聚类 日志预处理 |
Web document clustering based on web-log mining |
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| GAO Zhe,WEI Hai-ping,WANG Fu-wei,ZHAO Xiao-bi.Web document clustering based on web-log mining[J].Computer Engineering and Design,2008,29(18). |
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| Authors: | GAO Zhe WEI Hai-ping WANG Fu-wei ZHAO Xiao-bi |
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| Affiliation: | GAO Zhe,WEI Hai-ping,WANG Fu-wei,ZHAO Xiao-bi(School of Computer , Communication Engineering,Liaoning Shihua University,Funshun 113001,China) |
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| Abstract: | Web log mining is one of the web mining.The process of the web log mining and the k-means algorithms are introduced.And the shortage of the k-means algorithm is analyzed.The k-means algorithm needs to compute the distance between every data object and the center of the clusters,which lowers the efficiency.To this problem,an enhanced algorithm of the k-means is put forward,which avoids computing the distance between every data object and the center of the clusters.Web document clustering is implemented with ... |
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| Keywords: | k-means |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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