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dc.contributor.authorTran, N.
dc.contributor.authorVo, Ba Tuong
dc.contributor.authorPhung, D.
dc.contributor.authorVo, Ba-Ngu
dc.identifier.citationTran, N. and Vo, B.T. and Phung, D. and Vo, B. 2017. Clustering for point pattern data, pp. 3174-3179.

© 2016 IEEE. 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.

dc.titleClustering for point pattern data
dc.typeConference Paper
dcterms.source.titleProceedings - International Conference on Pattern Recognition
dcterms.source.seriesProceedings - International Conference on Pattern Recognition
curtin.departmentDepartment of Electrical and Computer Engineering
curtin.accessStatusFulltext not available

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