An Interactive Approach to Building Classiffication Models by Clustering and Cluster Validation

Zhexue Huang, Michael K. Ng, Tao Lin, David Cheung

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

18 Citations (Scopus)

Abstract

This paper presents the decision clusters classifier (DCC) for database mining. A DCC model consists of a small set of decision clusters extracted from a tree of clusters generated by a clustering algorithm from the training data set. A decision cluster is associated to one of the classes in the data set and used to determine the class of new objects. A DCC model classifies new objects by deciding which decision clusters these objects belong to. In making classification decisions, DCC is similar to the k-nearest neighbor classification scheme but its model building process is different. In this paper, we describe an interactive approach to building DCC models by stepwise clustering the training data set and validating the clusters using data visualization techniques. Our initial results on some public benchmarking data sets have shown that DCC models outperform the some existing popular classification methods.

Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning - IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents
Subtitle of host publicationSecond International Conference Shatin, N.T., Hong Kong, China, December 13-15, 2000. Proceedings
EditorsKwong Sak Leung, Lai-Wan Chan, Helen Meng
Place of PublicationBerlin
PublisherSpringer
Pages23-28
Number of pages6
Edition1st
ISBN (Electronic)9783540444916
ISBN (Print)9783540414506
DOIs
Publication statusPublished - 29 Nov 2000
Event2nd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2000 - Hong Kong, Hong Kong
Duration: 13 Dec 200015 Dec 2000
https://link.springer.com/book/10.1007/3-540-44491-2 (Conference Proceedings)

Publication series

NameLecture Notes in Computer Science
Volume1983
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2000
Country/TerritoryHong Kong
CityHong Kong
Period13/12/0015/12/00
Internet address

Scopus Subject Areas

  • Theoretical Computer Science
  • General Computer Science

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