Clustering for point pattern data

Nhat Quang Tran, Ba-Ngu Vo, Dinh Phung, Ba-Tuong Vo

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

5 Citations (Scopus)


Clustering is one of the most common unsupervised learning tasks in machine learning and data mining. Clustering algorithms have been used in a plethora of applications across several scientific fields. However, there has been limited research in the clustering of point patterns - sets or multi-sets of unordered elements - that are found in numerous applications and data sources. In this paper, we propose two approaches for clustering point patterns. The first is a non-parametric method based on novel distances for sets. The second is a model-based approach, formulated via random finite set theory, and solved by the Expectation-Maximization algorithm. Numerical experiments show that the proposed methods perform well on both simulated and real data.

Original languageEnglish
Title of host publication2016 23rd International Conference on Pattern Recognition (ICPR 2016)
EditorsLarry Davis, Alberto Del Bimbo, Brian C. Lovell
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781509048472
ISBN (Print)9781509048489
Publication statusPublished - 2016
Externally publishedYes
EventInternational Conference on Pattern Recognition 2016 - Cancun, Mexico
Duration: 4 Dec 20168 Dec 2016
Conference number: 23rd


ConferenceInternational Conference on Pattern Recognition 2016
Abbreviated titleICPR 2016
Internet address


  • Affinity propagation
  • Clustering
  • Expectation-maximization
  • Multiple instance data
  • Point pattern data
  • Point process
  • Random finite set

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