Clustering for point pattern data
© 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...
| Main Authors: | , , , |
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| Format: | Conference Paper |
| Published: |
2017
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| Online Access: | http://hdl.handle.net/20.500.11937/55340 |
| _version_ | 1848759595833491456 |
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| author | Tran, N. Vo, Ba Tuong Phung, D. Vo, Ba-Ngu |
| author_facet | Tran, N. Vo, Ba Tuong Phung, D. Vo, Ba-Ngu |
| author_sort | Tran, N. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 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. |
| first_indexed | 2025-11-14T10:02:23Z |
| format | Conference Paper |
| id | curtin-20.500.11937-55340 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:02:23Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-553402018-03-29T09:09:27Z Clustering for point pattern data Tran, N. Vo, Ba Tuong Phung, D. Vo, Ba-Ngu © 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. 2017 Conference Paper http://hdl.handle.net/20.500.11937/55340 10.1109/ICPR.2016.7900123 restricted |
| spellingShingle | Tran, N. Vo, Ba Tuong Phung, D. Vo, Ba-Ngu Clustering for point pattern data |
| title | Clustering for point pattern data |
| title_full | Clustering for point pattern data |
| title_fullStr | Clustering for point pattern data |
| title_full_unstemmed | Clustering for point pattern data |
| title_short | Clustering for point pattern data |
| title_sort | clustering for point pattern data |
| url | http://hdl.handle.net/20.500.11937/55340 |