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
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
Computer Science, Artificial Intelligence
Computer Science
multiple instance data
random finite set
affinity propagation
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
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