Unsupervised multidimensional hierarchical clustering

Rakesh Dugad, Narendra Ahuja

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

Abstract

A method for multidimensional hierarchical clustering that is invariant to monotonic transformations of the distance metric is presented. The method derives a tree of clusters organized according to the homogeneity of intracluster and interpoint distances. Higher levels correspond to coarser clusters. At any level the method can detect clusters of different densities, shapes and sizes. The number of clusters and the parameters for clustering are determined automatically and adaptively for a given data set which makes it unsupervised and non-parametric. The method is simple, noniterative and requires low computation. Results on various sample data sets are presented.

Original languageEnglish (US)
Title of host publicationProceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 1998
Pages2761-2764
Number of pages4
DOIs
StatePublished - Dec 1 1998
Event1998 23rd IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 1998 - Seattle, WA, United States
Duration: May 12 1998May 15 1998

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume5
ISSN (Print)1520-6149

Other

Other1998 23rd IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 1998
CountryUnited States
CitySeattle, WA
Period5/12/985/15/98

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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