NDLI: Online and incremental mining of separately-grouped Web access logs

Content Provider	IEEE Xplore Digital Library
Author	Yew-Kwong Woon Wee-Keong Ng Ee-Peng Lim
Copyright Year	2002
Description	Author affiliation: Nanyang Technol. Univ., Singapore, Singapore (Yew-Kwong Woon; Wee-Keong Ng; Ee-Peng Lim)
Abstract	The rising popularity of electronic commerce makes data mining an indispensable technology for business competitiveness. The World Wide Web provides abundant raw data in the form of Web access logs, Web transaction logs and Web user profiles. Without data mining tools, it is impossible to make any sense of such massive data. We focus on Web usage mining because it deals most appropriately with understanding user behavioral patterns which is the key to successful customer relationship management. Previous work dealt separately with specific issues of Web usage mining and made assumptions without taking a holistic view and thus, had limited practical applicability. We formulate a novel and more holistic version of Web usage mining termed transactionized logfile mining (TRALOM) to effectively and correctly identify transactions as well as to mine useful knowledge from Web access logs. We also introduce a new data structure, called the WebTrie, to efficiently hold useful preprocessed data so that TRALOM can be done in an online and incremental fashion. Experiments conducted on real Web server logs verify the usefulness and practicality of our proposed techniques.
Sponsorship	Software AG
Starting Page	53
Ending Page	62
File Size	388444
Page Count	10
File Format	PDF
ISBN	0769517668
DOI	10.1109/WISE.2002.1181643
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-12-14
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data mining Web mining Web server Electronic commerce Customer relationship management Information analysis Web sites Data structures Inspection Data analysis
Content Type	Text
Resource Type	Article

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