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Please use this identifier to cite or link to this item: http://hdl.handle.net/10112/6935

Title: Character String Analysis and Customer Path in Stream Data
Authors: YADA, Katsutoshi
Author's alias: 矢田, 勝俊
Keywords: character string analysis
customer path
data mining
stream data
Issue Date: Dec-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Shimei: Proceedings. IEEE International Conference on Data Mining Workshops ICDM Workshops 2008
Start page: 829
End page: 836
Abstract: This purpose of this study is to propose a knowledge-discovery system that can abstract helpful information from character strings representing shopper visits to product sections associated with positive and negative purchasing events by applying character string parsing technologies to stream data describing customer purchasing behavior inside a store. Taking data that traced customers' movements we focus on the number of times customers stop by particular product sections, and by representing those visits in the form of character strings, we propose a way to efficiently handle large stream data. During our experiment, we abstract store-section visiting patterns that characterize customers who purchase a relatively larger volume of items, and are able to show the usefulness of these visiting patterns. In addition, we examine index functions, calculation time, and prediction accuracy, and clarify technological issues warranting further research. In the present study, we demonstrate the feasibility of employing stream data in the marketing field and the usefulness of the employing character parsing techniques.
Description: IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008, 15-19 December 2008, Pisa, Italy
type: Conference Paper
Rights: (C) 2008 IEEE. Reprinted, with permission, from YADA Katsutoshi, Character String Analysis and Customer Path in Stream Data, 12/2008. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Kansai University'sproducts or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org.
URI: http://hdl.handle.net/10112/6935
ISBN: 9780769535036
Text Version: publisher
Appears in Collections:商学部-会議発表論文

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