A framework for segmenting customers based on probability density of transaction data

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

抄録

Segmenting customers based on transaction data contributes to better understanding and characterizing customers, and has drawn a great deal of attention in literature of various fields. Data mining literature has provided various clustering algorithms for customer segmentation, and distance measure plays an important role in many approaches. However, most distance measures are based on co-occurrence of items, and pay few attention to the sales volume or quantities of items in transactions. In this paper, the probability density of items is employed to gather the description information of transactions and calculate the distance between transactions. Based on distinguishing the difference between similarity measures for transactions and customers, set distance is employed to evaluate the similarity between customers. The whole process is introduced as a framework to reach the target of segmenting customers.

本文言語英語
ホスト出版物のタイトルProceedings of the 2012 IIAI International Conference on Advanced Applied Informatics, IIAIAAI 2012
ページ273-278
ページ数6
DOI
出版ステータス出版済み - 12月 14 2012
イベント1st IIAI International Conference on Advanced Applied Informatics, IIAIAAI 2012 - Fukuoka, 日本
継続期間: 9月 20 20129月 22 2012

出版物シリーズ

名前Proceedings of the 2012 IIAI International Conference on Advanced Applied Informatics, IIAIAAI 2012

その他

その他1st IIAI International Conference on Advanced Applied Informatics, IIAIAAI 2012
国/地域日本
CityFukuoka
Period9/20/129/22/12

!!!All Science Journal Classification (ASJC) codes

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