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: Daiji Tanaka, Katsuhiro Honda, Seiki Ubukata, Akira Notsu
Format: Article
Language:English
Published: Wiley 2016-01-01
Series:Advances in Fuzzy Systems
Online Access:http://dx.doi.org/10.1155/2016/5206048
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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.
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institution Kabale University
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publishDate 2016-01-01
publisher Wiley
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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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