Uniformity and homogeneity-based hierarchical clustering

Peter Bajcsy, Narendra Ahuja

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

Abstract

This paper presents a clustering algorithm for dot patterns in n-dimensional space. The n-dimensional space often represents a multivariate (nf-dimensional) function in a ns-dimensional space (ns+nf=n). The proposed algorithm decomposes the clustering problem into the two lower dimensional problems. Clustering in n f-dimensional space is performed to detect the sets of dots in n-dimensional space having similar nf-variate function values (location based clustering using a homogeneity model). Clustering in n s dimensional space is performed to detect the sets of dots in n-dimensional space having similar interneighbor distances (density based clustering with a uniformity model). Clusters in the n-dimensional space are obtained by combining the results in the two subspaces.

Original languageEnglish (US)
Title of host publicationTrack B
Subtitle of host publicationPattern Recognition and Signal Analysis
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages96-100
Number of pages5
ISBN (Print)081867282X, 9780818672828
DOIs
StatePublished - Jan 1 1996
Event13th International Conference on Pattern Recognition, ICPR 1996 - Vienna, Austria
Duration: Aug 25 1996Aug 29 1996

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume2
ISSN (Print)1051-4651

Other

Other13th International Conference on Pattern Recognition, ICPR 1996
Country/TerritoryAustria
CityVienna
Period8/25/968/29/96

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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