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