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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 2012 IIAI International Conference on Advanced Applied Informatics, IIAIAAI 2012
Pages273-278
Number of pages6
DOIs
Publication statusPublished - Dec 14 2012
Event1st IIAI International Conference on Advanced Applied Informatics, IIAIAAI 2012 - Fukuoka, Japan
Duration: Sep 20 2012Sep 22 2012

Publication series

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

Other

Other1st IIAI International Conference on Advanced Applied Informatics, IIAIAAI 2012
Country/TerritoryJapan
CityFukuoka
Period9/20/129/22/12

All Science Journal Classification (ASJC) codes

  • Information Systems

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