A top-down approach for hierarchical cluster exploration by visualization

Ke Bing Zhang*, Mehmet A. Orgun, Peter A. Busch, Abhaya C. Nayak

*Corresponding author for this work

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

2 Citations (Scopus)


With the much increased capability of data collection and storage in the past decade, data miners have to deal with much larger datasets in knowledge discovery tasks. Very large observations may cause traditional clustering methods to break down and not be able to cope with such large volumes of data. To enable data miners effectively detect the hierarchical cluster structure of a very large dataset, we introduce a visualization technique HOV3 to plot the dataset into clear and meaningful subsets by using its statistical summaries. Therefore, data miners can focus on investigating a relatively smaller-sized subset and its nested clusters. In such a way, data miners can explore clusters of any subset and its offspring subsets in a top-down fashion. As a consequence, HOV3 provides data miners an effective method on the exploration of clusters in a hierarchy by visualization.

Original languageEnglish
Title of host publicationAdvanced Data Mining and Applications - 6th International Conference, ADMA 2010, Proceedings
EditorsLongbing Cao, Yong Feng, Jiang Zhong
Place of PublicationBerlin ; New York
PublisherSpringer, Springer Nature
Number of pages12
Volume6440 LNAI
EditionPART 1
ISBN (Print)3642173152, 9783642173158
Publication statusPublished - 2010
Event6th International Conference on Advanced Data Mining and Applications, ADMA 2010 - Chongqing, China
Duration: 19 Nov 201021 Nov 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6440 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349


Other6th International Conference on Advanced Data Mining and Applications, ADMA 2010


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