Enhanced visual separation of clusters by M-mapping to facilitate cluster analysis

Ke Bing Zhang*, Mehmet A. Orgun, Kang Zhang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

2 Citations (Scopus)

Abstract

The goal of clustering in data mining is to distinguish objects into partitions/clusters based on given criteria. Visualization methods and techniques may provide users an intuitively appealing interpretation of cluster structures. Having good visually separated groups of the studied data is beneficial for detecting cluster information as well as refining the membership formation of clusters. In this paper, we propose a novel visual approach called M-mapping, based on the projection technique of HOV3 to achieve the separation of cluster structures. With M-mapping, users can explore visual cluster clues intuitively and validate clusters effectively by matching the geometrical distributions of clustered and non-clustered subsets produced in HOV3.

Original languageEnglish
Title of host publicationAdvances in Visual Information Systems - 9th International Conference, VISUAL 2007, Revised Selected Papers
EditorsGuoping Qui, Clement Leung, Xiangyang Xue, Robert Laurini
Place of PublicationBerlin; Heidelberg
PublisherSpringer, Springer Nature
Pages285-297
Number of pages13
Volume4781 LNCS
ISBN (Print)9783540764137
Publication statusPublished - 2007
Event9th International Conference on Visual Information Systems, VISUAL 2007 - Shanghai, China
Duration: 28 Jun 200729 Jun 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4781 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other9th International Conference on Visual Information Systems, VISUAL 2007
Country/TerritoryChina
CityShanghai
Period28/06/0729/06/07

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