A Semi-Supervised Framework for MMMs-Induced Fuzzy Co-Clustering with Virtual Samples
Although the goal of clustering is to reveal structural information from unlabeled datasets, in cases with partial structural supervisions, semi-supervised clustering is expected to improve partition quality. However, in many real applications, it may cause additional costs to provide an enough amou...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-01-01
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| Series: | Advances in Fuzzy Systems |
| Online Access: | http://dx.doi.org/10.1155/2016/5206048 |
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| _version_ | 1832548325926109184 |
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| author | Daiji Tanaka Katsuhiro Honda Seiki Ubukata Akira Notsu |
| author_facet | Daiji Tanaka Katsuhiro Honda Seiki Ubukata Akira Notsu |
| author_sort | Daiji Tanaka |
| collection | DOAJ |
| description | Although the goal of clustering is to reveal structural information from unlabeled datasets, in cases with partial structural supervisions, semi-supervised clustering is expected to improve partition quality. However, in many real applications, it may cause additional costs to provide an enough amount of supervised objects with class labels. A virtual sample approach is a practical technique for improving classification quality in semi-supervised learning, in which additional virtual samples are generated from supervised objects. In this research, the virtual sample approach is adopted in semi-supervised fuzzy co-clustering, where the goal is to reveal object-item pairwise cluster structures from cooccurrence information among them. Several experimental results demonstrate the characteristics of the proposed approach. |
| format | Article |
| id | doaj-art-53657db42ff046f591fa3dde7f7ae687 |
| institution | Kabale University |
| issn | 1687-7101 1687-711X |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Fuzzy Systems |
| spelling | doaj-art-53657db42ff046f591fa3dde7f7ae6872025-02-03T06:14:18ZengWileyAdvances in Fuzzy Systems1687-71011687-711X2016-01-01201610.1155/2016/52060485206048A Semi-Supervised Framework for MMMs-Induced Fuzzy Co-Clustering with Virtual SamplesDaiji Tanaka0Katsuhiro Honda1Seiki Ubukata2Akira Notsu3Graduate School of Engineering, Osaka Prefecture University, Sakai, Osaka 599-8531, JapanGraduate School of Engineering, Osaka Prefecture University, Sakai, Osaka 599-8531, JapanGraduate School of Engineering, Osaka Prefecture University, Sakai, Osaka 599-8531, JapanGraduate School of Engineering, Osaka Prefecture University, Sakai, Osaka 599-8531, JapanAlthough the goal of clustering is to reveal structural information from unlabeled datasets, in cases with partial structural supervisions, semi-supervised clustering is expected to improve partition quality. However, in many real applications, it may cause additional costs to provide an enough amount of supervised objects with class labels. A virtual sample approach is a practical technique for improving classification quality in semi-supervised learning, in which additional virtual samples are generated from supervised objects. In this research, the virtual sample approach is adopted in semi-supervised fuzzy co-clustering, where the goal is to reveal object-item pairwise cluster structures from cooccurrence information among them. Several experimental results demonstrate the characteristics of the proposed approach.http://dx.doi.org/10.1155/2016/5206048 |
| spellingShingle | Daiji Tanaka Katsuhiro Honda Seiki Ubukata Akira Notsu A Semi-Supervised Framework for MMMs-Induced Fuzzy Co-Clustering with Virtual Samples Advances in Fuzzy Systems |
| title | A Semi-Supervised Framework for MMMs-Induced Fuzzy Co-Clustering with Virtual Samples |
| title_full | A Semi-Supervised Framework for MMMs-Induced Fuzzy Co-Clustering with Virtual Samples |
| title_fullStr | A Semi-Supervised Framework for MMMs-Induced Fuzzy Co-Clustering with Virtual Samples |
| title_full_unstemmed | A Semi-Supervised Framework for MMMs-Induced Fuzzy Co-Clustering with Virtual Samples |
| title_short | A Semi-Supervised Framework for MMMs-Induced Fuzzy Co-Clustering with Virtual Samples |
| title_sort | semi supervised framework for mmms induced fuzzy co clustering with virtual samples |
| url | http://dx.doi.org/10.1155/2016/5206048 |
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