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arXiv · 1702.02262

Clustering For Point Pattern Data

Quang N. Tran·Ba-Ngu Vo·Dinh Phung·Ba-Tuong Vo
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Abstract

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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Quang N. Tran, Ba-Ngu Vo, Dinh Phung, Ba-Tuong Vo. 2017-02-08. Clustering For Point Pattern Data. https://arxiv.org/abs/1702.02262

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