書誌事項
- タイトル別名
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- An Approach of Vectorizing Shopping Paths Sensed by RFID Tags to Classify Retail Customers and its Application with Principal Component Regression
- カイモノ ケイロ ノ ベクトルカ ニ モトズク コキャク ハンベツ アプローチ オヨビ シュセイブン カイキ オ モチイタ テキヨウレイ
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抄録
In this paper, we propose an approach for classifying customers in retail stores into given types according to their shopping paths, each of which is a sequence of sections visited by the corresponding customer and is gathered by an RFID tag. The approach vectorizes a sequence of sections; that is, the approach splits such a sequence into tuples of sections, then sums up occurring counts of those tuples. This vectorization is based on the hypothesis that a customer's type has relation to sub-sequences of sections in his/her shopping path and a conjecture that customers' types can be attributed to co-occurrences of such sub-sequences. After vectorization, the proposed approach applies a general discrimination method to such vectors of equal length.<br>In computational illustrations, the principal component regression is selected as a representative of general discrimination methods and is applied to shopping paths collected in an existing retail store so as to predict whether a customer purchases items much than average or not. Computational results display the effectiveness of the proposed approach as higher forecast accuracies than known works.
収録刊行物
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- 電気学会論文誌C(電子・情報・システム部門誌)
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電気学会論文誌C(電子・情報・システム部門誌) 132 (12), 2051-2058, 2012
一般社団法人 電気学会
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詳細情報 詳細情報について
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- CRID
- 1390282679585181312
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- NII論文ID
- 10031129682
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- NII書誌ID
- AN10065950
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- ISSN
- 13488155
- 03854221
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- NDL書誌ID
- 024253864
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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
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可