A fuzzy approach for multitype relational data clustering
Mining interrelated data among multiple types of objects or entities is important in many real-world applications. Despite extensive study on fuzzy clustering of vector space data, very limited exploration has been made on fuzzy clustering of relational data that involve several object types. In thi...
| Main Authors: | , |
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| Format: | Journal Article |
| Language: | English |
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2013
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| Online Access: | https://hdl.handle.net/10356/102712 http://hdl.handle.net/10220/16480 |
| _version_ | 1811678232723849216 |
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| author | Mei, Jian-Ping Chen, Lihui |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Mei, Jian-Ping Chen, Lihui |
| author_sort | Mei, Jian-Ping |
| collection | NTU |
| description | Mining interrelated data among multiple types of objects or entities is important in many real-world applications. Despite extensive study on fuzzy clustering of vector space data, very limited exploration has been made on fuzzy clustering of relational data that involve several object types. In this paper, we propose a new fuzzy clustering approach for multitype relational data (FC-MR). In FC-MR, different types of objects are clustered simultaneously. An object is assigned a large membership with respect to a cluster if its related objects in this cluster have high rankings. In each cluster, an object tends to have a high ranking if its related objects have large memberships in this cluster. The FC-MR approach is formulated to deal with multitype relational data with various structures. The objective function of FC-MR is locally optimized by an efficient iterative algorithm, which updates the fuzzy membership matrix and the ranking matrix of one type at once while keeping those of other types constant. We also discuss the simplified FC-MR for multitype relational data with two special structures, namely, star-structure and extended star-structure. Experimental studies are conducted on benchmark document datasets to illustrate how the proposed approach can be applied flexibly under different scenarios in real-world applications. The experimental results demonstrate the feasibility and effectiveness of the new approach compared with existing ones. |
| first_indexed | 2024-10-01T02:50:00Z |
| format | Journal Article |
| id | ntu-10356/102712 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:50:00Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/1027122020-03-07T14:00:35Z A fuzzy approach for multitype relational data clustering Mei, Jian-Ping Chen, Lihui School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering Mining interrelated data among multiple types of objects or entities is important in many real-world applications. Despite extensive study on fuzzy clustering of vector space data, very limited exploration has been made on fuzzy clustering of relational data that involve several object types. In this paper, we propose a new fuzzy clustering approach for multitype relational data (FC-MR). In FC-MR, different types of objects are clustered simultaneously. An object is assigned a large membership with respect to a cluster if its related objects in this cluster have high rankings. In each cluster, an object tends to have a high ranking if its related objects have large memberships in this cluster. The FC-MR approach is formulated to deal with multitype relational data with various structures. The objective function of FC-MR is locally optimized by an efficient iterative algorithm, which updates the fuzzy membership matrix and the ranking matrix of one type at once while keeping those of other types constant. We also discuss the simplified FC-MR for multitype relational data with two special structures, namely, star-structure and extended star-structure. Experimental studies are conducted on benchmark document datasets to illustrate how the proposed approach can be applied flexibly under different scenarios in real-world applications. The experimental results demonstrate the feasibility and effectiveness of the new approach compared with existing ones. 2013-10-14T06:44:31Z 2019-12-06T20:59:29Z 2013-10-14T06:44:31Z 2019-12-06T20:59:29Z 2012 2012 Journal Article Mei, J.-P., & Chen, L. (2012). A Fuzzy Approach for Multitype Relational Data Clustering. IEEE Transactions on Fuzzy Systems, 20(2), 358-371. https://hdl.handle.net/10356/102712 http://hdl.handle.net/10220/16480 10.1109/TFUZZ.2011.2174366 en IEEE transactions on fuzzy systems |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Mei, Jian-Ping Chen, Lihui A fuzzy approach for multitype relational data clustering |
| title | A fuzzy approach for multitype relational data clustering |
| title_full | A fuzzy approach for multitype relational data clustering |
| title_fullStr | A fuzzy approach for multitype relational data clustering |
| title_full_unstemmed | A fuzzy approach for multitype relational data clustering |
| title_short | A fuzzy approach for multitype relational data clustering |
| title_sort | fuzzy approach for multitype relational data clustering |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/102712 http://hdl.handle.net/10220/16480 |
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