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Concept Lattice Reduction Using Integer Programming
https://ipsj.ixsq.nii.ac.jp/records/239384
https://ipsj.ixsq.nii.ac.jp/records/239384e781f76b-177d-4351-b328-dd7db8ed7129
| 名前 / ファイル | ライセンス | アクション |
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IPSJ-JNL6509030.pdf (4.7 MB)
2026年9月15日からダウンロード可能です。
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Copyright (c) 2024 by the Information Processing Society of Japan
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| 非会員：¥0, IPSJ:学会員：¥0, 論文誌:会員：¥0, DLIB:会員：¥0 | ||
| Item type | Journal(1) | |||||||||
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| 公開日 | 2024-09-15 | |||||||||
| タイトル | ||||||||||
| タイトル | Concept Lattice Reduction Using Integer Programming | |||||||||
| タイトル | ||||||||||
| 言語 | en | |||||||||
| タイトル | Concept Lattice Reduction Using Integer Programming | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | [一般論文] formal concept analysis, object reduction, concept lattice reduction, approximation, integer linear programming, knowledge discovery | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||
| 資源タイプ | journal article | |||||||||
| 著者所属 | ||||||||||
| Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University | ||||||||||
| 著者所属 | ||||||||||
| Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University | ||||||||||
| 著者名 |
Siqi, Peng
× Siqi, Peng
× Akihiro, Yamamoto
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| 著者名(英) |
Siqi, Peng
× Siqi, Peng
× Akihiro, Yamamoto
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| 論文抄録 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | Concept lattice reduction is an important task related to formal concept analysis (FCA), a technique for knowledge extraction from binary relational data. Concept lattice reduction aims to approximate the input of FCA, called formal contexts, into simpler ones so that the volume and complexity of the output of FCA, called formal concepts, will also be reduced. A widely-preferred strategy for the task is object reduction, which approximates the input context by merging similar objects in it. While many methods were developed based on this strategy, we found that they may have two problems. First, the “approximate” context generated by such methods may introduce too many unnecessary modifications to the original one, making it hard to be considered a proper approximation. Second, these methods may unexpectedly insert or eliminate some concepts after reduction, which may cause significant changes to the knowledge extracted by FCA. To solve these problems, we introduce a new method using integer linear programming, which translates concept lattice reduction into an integer linear optimization problem and can suppress the changes caused to the input contexts and extracted concepts by adding corresponding linear constraints. We conduct experiments on several data sets to prove that our method works. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.32(2024) (online) DOI http://dx.doi.org/10.2197/ipsjjip.32.844 ------------------------------ |
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| 論文抄録(英) | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | Concept lattice reduction is an important task related to formal concept analysis (FCA), a technique for knowledge extraction from binary relational data. Concept lattice reduction aims to approximate the input of FCA, called formal contexts, into simpler ones so that the volume and complexity of the output of FCA, called formal concepts, will also be reduced. A widely-preferred strategy for the task is object reduction, which approximates the input context by merging similar objects in it. While many methods were developed based on this strategy, we found that they may have two problems. First, the “approximate” context generated by such methods may introduce too many unnecessary modifications to the original one, making it hard to be considered a proper approximation. Second, these methods may unexpectedly insert or eliminate some concepts after reduction, which may cause significant changes to the knowledge extracted by FCA. To solve these problems, we introduce a new method using integer linear programming, which translates concept lattice reduction into an integer linear optimization problem and can suppress the changes caused to the input contexts and extracted concepts by adding corresponding linear constraints. We conduct experiments on several data sets to prove that our method works. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.32(2024) (online) DOI http://dx.doi.org/10.2197/ipsjjip.32.844 ------------------------------ |
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| 書誌レコードID | ||||||||||
| 収録物識別子タイプ | NCID | |||||||||
| 収録物識別子 | AN00116647 | |||||||||
| 書誌情報 |
情報処理学会論文誌 巻 65, 号 9, 発行日 2024-09-15 |
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| ISSN | ||||||||||
| 収録物識別子タイプ | ISSN | |||||||||
| 収録物識別子 | 1882-7764 | |||||||||
| 公開者 | ||||||||||
| 言語 | ja | |||||||||
| 出版者 | 情報処理学会 | |||||||||
