DocumentCode :
3169118
Title :
Mining E-Commerce Data to Analyze the Target Customer Behavior
Author :
Jiang, Yuantao ; Yu, Siqin
Author_Institution :
Shanghai Maritime Univ., Shanghai
fYear :
2008
fDate :
23-24 Jan. 2008
Firstpage :
406
Lastpage :
409
Abstract :
In the advent of the information era, e-commerce has developed rapidly and has become significant for every business. With the advanced information technologies, firms are now able to collect and store mountains of data describing their myriad offerings and diverse customer profiles, from which they seek to derive information about their customers´ needs and wants. Traditional forecasting methods are no longer suitable for these business situations. This research used the principles of data mining to cluster customer segments by using k-means algorithm and data from Web log of various e-commerce Websites. Consequently, the results showed that there was a clear distinction between the segments in terms of customer behavior.
Keywords :
customer satisfaction; data mining; electronic commerce; Web log; e-commerce data mining; forecasting methods; k-means algorithm; Clustering algorithms; Data analysis; Data mining; Databases; Electronic commerce; HTML; Information analysis; Information technology; Navigation; Production facilities;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Knowledge Discovery and Data Mining, 2008. WKDD 2008. First International Workshop on
Conference_Location :
Adelaide, SA
Print_ISBN :
978-0-7695-3090-1
Type :
conf
DOI :
10.1109/WKDD.2008.90
Filename :
4470425
Link To Document :
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