# Clustering For Point Pattern Data

## TL;DR

This paper introduces two novel clustering methods for point pattern data, addressing a gap in existing algorithms, and demonstrates their effectiveness on simulated and real datasets.

## Contribution

It proposes a non-parametric distance-based method and a model-based approach using finite set theory for clustering point patterns.

## Key findings

- Methods perform well on simulated data
- Methods are effective on real-world data
- New approaches outperform existing techniques

## 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.