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An efficient cell-based clustering method for handling large, high-dimensional data

  • Engineering Research Institute

Research output: Contribution to conferenceConference paperpeer-review

Abstract

In this paper, we propose an efficient cell-based clustering method for handling a large of amount of high-dimensional data. Our clustering method provides an efficient cell creation algorithm using a space-partitioning technique and a cell insertion algorithm to construct clusters as cells with more density than a given threshold. To achieve good retrieval performance on clusters, we also propose a new filtering-based index structure using an approximation technique. In addition, we compare the performance of our cell-based clustering method with the CLIQUE method in terms of cluster construction time, precision, and retrieval time. The experimental results show that our clustering method achieves better performance on cluster construction time and retrieval time.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining
EditorsKyu-Young Wang, Jongwoo Jeon, Kyuseok Shim, Jaideep Srivastava
PublisherSpringer Verlag
Pages295-300
Number of pages6
ISBN (Electronic)3540047603, 9783540047605
DOIs
StatePublished - 2003
Event7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003 - Seoul, Korea, Republic of
Duration: 2003.04.302003.05.2

Publication series

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

Conference

Conference7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003
Country/TerritoryKorea, Republic of
CitySeoul
Period03.04.3003.05.2

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