Visualizing transactional data with multiple clusterings for knowledge discovery

Nicolas Durand, Bruno Crémilleux, Einoshin Suzuki

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

2 Citations (Scopus)

Abstract

Information visualization is gaining importance in data mining and transactional data has long been an important target for data miners. We propose a novel approach for visualizing transactional data using multiple clustering results for knowledge discovery. This scheme necessitates us to relate different clustering results in a comprehensive manner. Thus we have invented a method for attributing colors to clusters of different clustering results based on minimal transversals. The effectiveness of our method VISUMCLUST has been confirmed with experiments using artificial and real-world data sets.

Original languageEnglish
Title of host publicationFoundations of Intelligent Systems - 16th International Symposium, ISMIS 2006, Proceedings
PublisherSpringer Verlag
Pages47-57
Number of pages11
ISBN (Print)354045764X, 9783540457640
DOIs
Publication statusPublished - 2006
Event16th International Symposium on Methodologies for Intelligent Systems, ISMIS 2006 - Bari, Italy
Duration: Sep 27 2006Sep 29 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4203 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other16th International Symposium on Methodologies for Intelligent Systems, ISMIS 2006
CountryItaly
CityBari
Period9/27/069/29/06

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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