A new clustering algorithm for transaction data via caucus

Jinmei Xu, Hui Xiong, Sam Yuan Sung, Vipin Kumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

The fast-growing large point of sale databases in stores and companies sets a pressing need for extracting high-level knowledge. Transaction clustering arises to receive attentions in recent years. However, traditional clustering techniques are not useful to solve this problem. Transaction data sets are different from the traditional data sets in their high dimensionality, sparsity and a large number of outliers. In this paper we present and experimentally evaluate a new efficient transaction clustering technique based on cluster of buyers called caucus that can be effectively used for identification of center of cluster. Experiments on real and synthetic data sets indicate that compare to prior work, caucus-based method can derive clusters of better quality as well as reduce the execution time considerably.

Original languageEnglish (US)
Title of host publicationAdvances in Knowledge Discovery and Data Mining
EditorsKyuseok Shim, Kyu-Young Wang, Jongwoo Jeon, Jaideep Srivastava
PublisherSpringer- Verlag
Pages551-562
Number of pages12
ISBN (Electronic)3540047603, 9783540047605
StatePublished - Jan 1 2003
Event7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003 - Seoul, Korea, Republic of
Duration: Apr 30 2003May 2 2003

Publication series

NameLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume2637
ISSN (Print)0302-9743

Other

Other7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003
CountryKorea, Republic of
CitySeoul
Period4/30/035/2/03

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