Location- and density-based hierarchical clustering using similarity analysis

Peter Bajcsy, Narendra Ahuja

Research output: Contribution to journalArticlepeer-review


This paper presents a new approach to hierarchical clustering of point patterns. Two algorithms for hierarchical location- and densitybased clustering are developed. Each method groups points such that maximum intracluster similarity and intercluster dissimilarity are achieved for point locations or point separations. Performance of the clustering methods is compared with four other methods. The approach is applied to a two-step texture analysis, where points represent centroid and average color of the regions in image segmentation.

Original languageEnglish (US)
Pages (from-to)1011-1015
Number of pages5
JournalIEEE transactions on pattern analysis and machine intelligence
Issue number9
StatePublished - 1998
Externally publishedYes


  • Clustering
  • Density-based clustering
  • Hierarchy of clusters
  • Location-based clustering
  • Point patterns
  • Spatially interleaved clusters

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


Dive into the research topics of 'Location- and density-based hierarchical clustering using similarity analysis'. Together they form a unique fingerprint.

Cite this