Systems support for scalable data mining
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
KDD-Cup 2000 organizers' report: peeling the onion
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
A Framework for Efficient and Anonymous Web Usage Mining Based on Client-Side Tracking
WEBKDD '01 Revised Papers from the Third International Workshop on Mining Web Log Data Across All Customers Touch Points
The use of web structure and content to identify subjectively interesting web usage patterns
ACM Transactions on Internet Technology (TOIT)
Lessons and Challenges from Mining Retail E-Commerce Data
Machine Learning
Mining interesting knowledge from weblogs: a survey
Data & Knowledge Engineering
A process of knowledge discovery from web log data: Systematization and critical review
Journal of Intelligent Information Systems
Decision trees for web log mining
Intelligent Data Analysis
Data mining research for customer relationship management systems: a framework and analysis
International Journal of Business Information Systems
Intelligent system applications in electronic tourism
Expert Systems with Applications: An International Journal
Improving the web usage analysis process: a UML model of the ETL process
WebKDD'04 Proceedings of the 6th international conference on Knowledge Discovery on the Web: advances in Web Mining and Web Usage Analysis
Improving effectiveness on clickstream data mining
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
A random indexing approach for web user clustering and web prefetching
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
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We show that the e-commerce domain can provide all the right ingredients for successful data mining. We describe an integrate architecture for supporting this integration. Thearchitecture can dramatically reduce the pre-processing, cleaning, and data understanding effort often documented to take 80%of the time in knowledge discovery projects. We emphasize the need for data collection at the application server layer (not the web server)in order to support logging of data and metadata that is essential to the discovery process. We describe the datatransformation bridges require from the transaction processing systems an customer event streams (e.g.,clickstreams) to the data warehouse. We detail the mining workbench, which needs to provide multiple views of the data through reporting, data mining algorithms, visualization, and OLAP. We conclude with a set of challenges.