期刊文献+

一种基于划分的动态聚类算法 被引量:16

Partition-based dynamic clustering algorithm
下载PDF
导出
摘要 聚类分析是数据挖掘的一个重要研究分支,已经提出了许多聚类算法,划分方法是其中之一。划分方法的缺点是要求事先给定聚类结果数,对初始划分和输入顺序敏感等。为克服这些缺陷,以划分方法为基础,提出了一种基于划分的动态聚类算法。该算法按密度从大到小,依距离选择较为分散的初始值,同时可以过滤噪声数据,并在聚类的过程中动态地改变聚类结果数,改善了聚类质量,获得了更自然的结果。 Clustering is a promising application area for many fields including data mining, statistical data analysis, pattern recognition, image processing, etc. Partitioning method is a clustering algorithm, which is sensible to initial partitions (values of k), initial values and input sequence. To overcome these disadvantages, a partition-based dynamic clustering algorithm is developed. At first, the data objects is sorted by their densities. Then some dispersive data objects is selected as initial cluster centers according to priority. At the same time, the outliers can be filtrated. And it changes the numbers of partitions during the clustering. The experiments demonstrate that the algorithm improves the partition method and gets the better results.
出处 《计算机工程与设计》 CSCD 北大核心 2005年第1期177-179,229,共4页 Computer Engineering and Design
关键词 聚类分析 数据挖掘 划分方法 K-MEANS clustering data mining partition method k-means
  • 相关文献

参考文献5

二级参考文献18

  • 1[1]Han J., Kamber M.Data Mining: Concepts and Techniques[M].Morgan Kaufmann Publishers, 2000.
  • 2[2]Zhang T., Ramakrishnan R., Livny M.BIRCH:An Efficient DataClustering Method for Very Large Databases[A].Proceeding of ACM SIGMOD Conference[C].Portland, Oregon, June,1996.103-114.
  • 3[3]Ester M., Kriegel H.-P., Sander J., et al.A Density-BasedAlgorithm for Discovering Clusters in Large Spatial Databases with Noise[A].Proceeding 2nd International Conference on Knowledge Discovery and Data Mining(KDD′96)[C].Portland, 1996.226-231.
  • 4[4]Kriegel H.-P., Seeger B., Beckmann N., et al.The R*-tree: An Efficient and Robust Access Method for Points and Rectangles[A].Proceeding ACM SIGMOD International Conference on Management of Data (SIGMOD′90)[C].Atlantic City,NJ,1990.322-331.
  • 5A K Jain,M N Murty,P J Flynn.Data clustering:A survey[J].ACM Computer Surv, 1999 ;31 : 264-323.
  • 6R Agrawal,J Gehrke,D Gonopolos et al.Automatic subspace clustering of high dimensional data for data mining applications[C].In :Proc 1995 ACM-SIGMOD Int Conf Management of Data,Seattle,WA,.1998:94-105.
  • 7R Agrawal,R Srikant.Privacy-preserving data mining[C].In :Proc 2000 ACM-SIGMOD Int'l Conf.Management of Data,Dallas,TX,2000:439-450.
  • 8P Bradley,U Fayyad,C Reina.Scaling Clustering Algorithms to Large Databases[C].In:Knowledge Discovery and Data Mining,AAAI Press, Menlo Park,Calif, 1998:9-15.
  • 9V Ganti et al.Clustering Large Datasets in Arbitrary Metric Spaces [C].In : Data Engineering, IEEECS Press, Los Alamitos, Calif, 1999 : 502-511.
  • 10M Goebel,L Gruenwald.A survey of data mining and knowledge discovery software tools[J].SIGKDD Explorations, 1999: ( 1 ) :20-33.

共引文献167

同被引文献127

引证文献16

二级引证文献74

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部