Clustering for point pattern data

Tran, NQ, Vo, BN, Phung, Quoc-Dinh and Vo, BT 2016, Clustering for point pattern data, in 2016 23rd International Conference on Pattern Recognition (ICPR 2016), IEEE, Piscataway, N.J., pp. 3174-3179, doi: 10.1109/ICPR.2016.7900123.

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Title Clustering for point pattern data
Author(s) Tran, NQ
Vo, BN
Phung, Quoc-DinhORCID iD for Phung, Quoc-Dinh orcid.org/0000-0002-9977-8247
Vo, BT
Conference name Pattern Recognition. Conference (23rd : 2016 : Cancun, Mexico)
Conference location Cancun, Mexico
Conference dates 2016/12/04 - 2016/12/08
Title of proceedings 2016 23rd International Conference on Pattern Recognition (ICPR 2016)
Editor(s) [Unknown],
Publication date 2016
Start page 3174
End page 3179
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) Clustering
point pattern data
point process
Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science
multiple instance data
random finite set
affinity propagation
expectation-maximization
INSTANCE
ALGORITHMS
cs.LG
stat.ML
Summary 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.
ISBN 9781509048472
ISSN 1051-4651
Language eng
DOI 10.1109/ICPR.2016.7900123
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2016, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30098515

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