A random finite set model for data clustering
The goal of data clustering is to partition data points into groups to optimize a given objective function. While most existing clustering algorithms treat each data point as vector, in many applications each datum is not a vector but a point pattern or a set of points. Moreover, many existing clust...
| Main Authors: | , |
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| Format: | Conference Paper |
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Institute of Electrical and Electronics Engineers Inc.
2014
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| Online Access: | http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6916264&action=search&sortType=&rowsPerPage=&searchField=Search_All&matchBoolean=true&queryText=((A%20random%20finite%20set%20model%20for%20data%20clustering)%20AND%20phung) http://hdl.handle.net/20.500.11937/25019 |
| _version_ | 1848751590437027840 |
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| author | Phung, D. Vo, Ba-Ngu |
| author_facet | Phung, D. Vo, Ba-Ngu |
| author_sort | Phung, D. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The goal of data clustering is to partition data points into groups to optimize a given objective function. While most existing clustering algorithms treat each data point as vector, in many applications each datum is not a vector but a point pattern or a set of points. Moreover, many existing clustering methods require the user to specify the number of clusters, which is not available in advance. This paper proposes a new class of models for data clustering that addresses set-valued data as well as unknown number of clusters, using a Dirichlet Process mixture of Poisson random finite sets. We also develop an efficient Markov Chain Monte Carlo posterior inference technique that can learn the number of clusters and mixture parameters automatically from the data. Numerical studies are presented to demonstrate the salient features of this new model, in particular its capacity to discover extremely unbalanced clusters in data. |
| first_indexed | 2025-11-14T07:55:09Z |
| format | Conference Paper |
| id | curtin-20.500.11937-25019 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:55:09Z |
| publishDate | 2014 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-250192017-01-30T12:46:19Z A random finite set model for data clustering Phung, D. Vo, Ba-Ngu The goal of data clustering is to partition data points into groups to optimize a given objective function. While most existing clustering algorithms treat each data point as vector, in many applications each datum is not a vector but a point pattern or a set of points. Moreover, many existing clustering methods require the user to specify the number of clusters, which is not available in advance. This paper proposes a new class of models for data clustering that addresses set-valued data as well as unknown number of clusters, using a Dirichlet Process mixture of Poisson random finite sets. We also develop an efficient Markov Chain Monte Carlo posterior inference technique that can learn the number of clusters and mixture parameters automatically from the data. Numerical studies are presented to demonstrate the salient features of this new model, in particular its capacity to discover extremely unbalanced clusters in data. 2014 Conference Paper http://hdl.handle.net/20.500.11937/25019 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6916264&action=search&sortType=&rowsPerPage=&searchField=Search_All&matchBoolean=true&queryText=((A%20random%20finite%20set%20model%20for%20data%20clustering)%20AND%20phung) http://purl.org/au-research/grants/arc/FT0991854 Institute of Electrical and Electronics Engineers Inc. restricted |
| spellingShingle | Phung, D. Vo, Ba-Ngu A random finite set model for data clustering |
| title | A random finite set model for data clustering |
| title_full | A random finite set model for data clustering |
| title_fullStr | A random finite set model for data clustering |
| title_full_unstemmed | A random finite set model for data clustering |
| title_short | A random finite set model for data clustering |
| title_sort | random finite set model for data clustering |
| url | http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6916264&action=search&sortType=&rowsPerPage=&searchField=Search_All&matchBoolean=true&queryText=((A%20random%20finite%20set%20model%20for%20data%20clustering)%20AND%20phung) http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6916264&action=search&sortType=&rowsPerPage=&searchField=Search_All&matchBoolean=true&queryText=((A%20random%20finite%20set%20model%20for%20data%20clustering)%20AND%20phung) http://hdl.handle.net/20.500.11937/25019 |