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    Clustering for point pattern data

    Access Status
    Fulltext not available
    Authors
    Tran, N.
    Vo, Ba Tuong
    Phung, D.
    Vo, Ba-Ngu
    Date
    2017
    Type
    Conference Paper
    
    Metadata
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    Citation
    Tran, N. and Vo, B.T. and Phung, D. and Vo, B. 2017. Clustering for point pattern data, pp. 3174-3179.
    Source Title
    Proceedings - International Conference on Pattern Recognition
    DOI
    10.1109/ICPR.2016.7900123
    ISBN
    9781509048472
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/55340
    Collection
    • Curtin Research Publications
    Abstract

    © 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.

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