Fast exact pairwise-nearest-neighbor algorithm using groups and clusters rejection criteria

Yi Ching Liaw, Jun Feng Lin, Shen Chuan Tai, Jim Z.C. Lai

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

Abstract

Pairwise-nearest-neighbor (PNN) is an effective method of data clustering, which can usually generate good clustering results, but with high computational complexity. In this paper, a new method is presented to reduce the computational complexity of the PNN algorithm through dividing clusters into groups of clusters and using projections of clusters on differential vectors of group pairs to reject impossible groups and clusters in the nearest neighbor finding process of a cluster. Experimental results show that the proposed algorithm can effectively reduce the computing time and number of distance calculations of the PNN algorithm for data sets from real images. It is noted that the proposed method generates the same clustering results as those produced using the PNN algorithm.

Original languageEnglish
Title of host publicationProceedings of the IASTED International Conference on Signal and Image Processing, SIP 2009
Pages101-104
Number of pages4
Publication statusPublished - 2009 Dec 1
EventIASTED International Conference on Signal and Image Processing, SIP 2009 - Honolulu, HI, United States
Duration: 2009 Aug 172009 Aug 19

Publication series

NameProceedings of the IASTED International Conference on Signal and Image Processing, SIP 2009

Other

OtherIASTED International Conference on Signal and Image Processing, SIP 2009
CountryUnited States
CityHonolulu, HI
Period09-08-1709-08-19

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Software
  • Electrical and Electronic Engineering

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