An Effective Clustering Algorithm in Large Transaction Databases
DOI:
Author:
Affiliation:

Clc Number:

Fund Project:

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials
  • |
  • Comments
    Abstract:

    Clustering of transactions can find potential useful patterns to improve the product profit. In this paper, a two-step clustering algorithm——CATD is proposed, applicable in large transaction databases. First, the database is divided into partitions in which transactions are partially clustered into a number of subclusters. A hierarchical clustering algorithm is used to control the distance between these subclusters. In the global clustering, a k-medoids clustering algorithm is performed on the subclusters to get a set of k global clusters and identify noise. The algorithm is feasible for large databases because it only scans the original databases once and the clustering process can be performed in main memory due to the partitioning scheme and the support vector representative of subclusters.

    Reference
    Related
    Cited by
Get Citation

陈宁,陈安,周龙骧.大规模交易数据库的一种有效聚类算法.软件学报,2001,12(4):475-484

Copy
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:July 28,2000
  • Revised:December 19,2000
  • Adopted:
  • Online:
  • Published:
You are the firstVisitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-4
Address:4# South Fourth Street, Zhong Guan Cun, Beijing 100190,Postal Code:100190
Phone:010-62562563 Fax:010-62562533 Email:jos@iscas.ac.cn
Technical Support:Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063