FCM-Type Fuzzy Coclustering for Three-Mode Cooccurrence Data: 3FCCM and 3Fuzzy CoDoK
Cocluster structure analysis is a basic technique for revealing intrinsic structural information from cooccurrence data among objects and items, in which coclusters are composed of mutually familiar pairs of objects and items. In many real applications, it is also the case that we have not only cooc...
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doaj-52801ba7cdd9492cba44992b7dfa520e2020-11-24T22:34:32ZengHindawi LimitedAdvances in Fuzzy Systems1687-71011687-711X2017-01-01201710.1155/2017/98421279842127FCM-Type Fuzzy Coclustering for Three-Mode Cooccurrence Data: 3FCCM and 3Fuzzy CoDoKKatsuhiro Honda0Yurina Suzuki1Seiki 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 Humanities and Sustainable System Sciences, Osaka Prefecture University, Sakai, Osaka 599-8531, JapanCocluster structure analysis is a basic technique for revealing intrinsic structural information from cooccurrence data among objects and items, in which coclusters are composed of mutually familiar pairs of objects and items. In many real applications, it is also the case that we have not only cooccurrence information among objects and items but also intrinsic relation among items and other ingredients. For example, in food preference analysis, users’ preferences on foods should be found considering not only user-food cooccurrences but also the implicit relation among users and cooking ingredients. In this paper, two FCM-type fuzzy coclustering models, that is, FCCM and Fuzzy CoDoK, are extended for revealing intrinsic cocluster structures from three-mode cooccurrence data, where the aggregation degree of three elements in each cocluster is maximized through iterative updating of three types of fuzzy memberships for objects, items, and ingredients. The characteristic features of the proposed methods are demonstrated through a numerical experiment.http://dx.doi.org/10.1155/2017/9842127 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Katsuhiro Honda Yurina Suzuki Seiki Ubukata Akira Notsu |
spellingShingle |
Katsuhiro Honda Yurina Suzuki Seiki Ubukata Akira Notsu FCM-Type Fuzzy Coclustering for Three-Mode Cooccurrence Data: 3FCCM and 3Fuzzy CoDoK Advances in Fuzzy Systems |
author_facet |
Katsuhiro Honda Yurina Suzuki Seiki Ubukata Akira Notsu |
author_sort |
Katsuhiro Honda |
title |
FCM-Type Fuzzy Coclustering for Three-Mode Cooccurrence Data: 3FCCM and 3Fuzzy CoDoK |
title_short |
FCM-Type Fuzzy Coclustering for Three-Mode Cooccurrence Data: 3FCCM and 3Fuzzy CoDoK |
title_full |
FCM-Type Fuzzy Coclustering for Three-Mode Cooccurrence Data: 3FCCM and 3Fuzzy CoDoK |
title_fullStr |
FCM-Type Fuzzy Coclustering for Three-Mode Cooccurrence Data: 3FCCM and 3Fuzzy CoDoK |
title_full_unstemmed |
FCM-Type Fuzzy Coclustering for Three-Mode Cooccurrence Data: 3FCCM and 3Fuzzy CoDoK |
title_sort |
fcm-type fuzzy coclustering for three-mode cooccurrence data: 3fccm and 3fuzzy codok |
publisher |
Hindawi Limited |
series |
Advances in Fuzzy Systems |
issn |
1687-7101 1687-711X |
publishDate |
2017-01-01 |
description |
Cocluster structure analysis is a basic technique for revealing intrinsic structural information from cooccurrence data among objects and items, in which coclusters are composed of mutually familiar pairs of objects and items. In many real applications, it is also the case that we have not only cooccurrence information among objects and items but also intrinsic relation among items and other ingredients. For example, in food preference analysis, users’ preferences on foods should be found considering not only user-food cooccurrences but also the implicit relation among users and cooking ingredients. In this paper, two FCM-type fuzzy coclustering models, that is, FCCM and Fuzzy CoDoK, are extended for revealing intrinsic cocluster structures from three-mode cooccurrence data, where the aggregation degree of three elements in each cocluster is maximized through iterative updating of three types of fuzzy memberships for objects, items, and ingredients. The characteristic features of the proposed methods are demonstrated through a numerical experiment. |
url |
http://dx.doi.org/10.1155/2017/9842127 |
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