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- 山下 遥
- 早稲田大学
書誌事項
- タイトル別名
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- Trend of the SQC Based on Data Clustering-Studies of Categorical Data Analysis Based on Principal Points for Multivariate Binary Distribution-
- データ ノ クラスタリング ニ モトズク SQC ノ ドウコウ : 2チガタ シュヨウテン カイセキホウ オ モチイタ カテゴリカルデータ ノ カイセキ ニ カンスル ケンキュウ
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抄録
In statistics,there are many studies on principal points.The concept of principal points which is proposed by Flury allows us to carry out such an analysis in a variety of applications and also properties of principal points have been studied. Although principal points of a multivariate distribution have widely studied,there is no discussion of principal points for a multivariate binary distribution.<BR> Yamashita and Suzuki have define the principal points for a multivariate binary distribution. Since principal points for a multivariate binary distribution are selected from multivariate binary region,there is a problem of the amount of calculation,since this problem is an NP-hard problem. Yamashita and Suzuki have shown the submodularity of principal points for a multivariate binary distribution and proposed an approximation method based on the greedy algorithm.Using the property of submodularity of principal points for a multivariate binary distribution,the accuracy of approximations is at least(1-1/e)times the optimal solution proved by Nemhauser et al.Finally, we show the result of an application of the methods to questionnaire survey data.
収録刊行物
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- 品質
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品質 46 (4), 387-392, 2016-10-15
一般社団法人 日本品質管理学会
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詳細情報 詳細情報について
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- CRID
- 1390001288145745792
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- NII論文ID
- 130007659809
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- NII書誌ID
- AN00354769
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- ISSN
- 24321044
- 03868230
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- NDL書誌ID
- 027689611
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- NDL
- CiNii Articles
- KAKEN
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- 抄録ライセンスフラグ
- 使用不可