Clustering For Point Pattern Data
This addresses a gap in clustering research for point patterns, which are common in various applications, but the work appears incremental as it builds on existing clustering and set theory concepts.
The paper tackles the problem of clustering point pattern data, which are sets of unordered elements, by proposing two methods: a non-parametric approach using novel set distances and a model-based approach using random finite set theory and EM. The results show that these methods perform well on simulated and real data.
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.