Clustering for point pattern data

By Nhat-Quang Tran, B. Vo, Dinh Q. Phung. 2017

13 citations recorded by the source corpus.

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.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Statistics and Probability, Probabilistic & Bayesian Methods

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Citations in the corpus

Learn to Predict Sets Using Feed-Forward Neural Networks (2020, background): “Approaches such as mixture models [31], [39], [40], learning distributions from a set of samples [41], [42], model-based multiple instance learning [43], and novelty detection from point pattern data [44], can be counted as few out many examples that use point patterns or sets as input or output and”
Model-Based Multiple Instance Learning (2017, background): “These developments have been partially reported in [15], [16], [17], respectively.”
Joint Learning of Set Cardinality and State Distribution (2017, methodology): “Approaches such as mixture models (Blei, Ng, and Jordan 2003; Hannah, Blei, and Powell 2011; Tran et al. 2016), learning distributions from a set of samples (Muandet et al. 2012; Oliva, Póczos, and Schneider 2013), model-based multiple instance learning (Vo et al. 2017) and novelty detection from…”