Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Using predictive prefetching to improve World Wide Web latency
ACM SIGCOMM Computer Communication Review
Using path profiles to predict HTTP requests
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Using association rules for product assortment decisions: a case study
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Unexpectedness as a measure of interestingness in knowledge discovery
Decision Support Systems - Special issue on WITS '97
Link prediction and path analysis using Markov chains
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Efficient mining of weighted association rules (WAR)
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Interactive path analysis of web site traffic
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Beyond Market Baskets: Generalizing Association Rules to Dependence Rules
Data Mining and Knowledge Discovery
A Microeconomic View of Data Mining
Data Mining and Knowledge Discovery
Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization
Data Mining and Knowledge Discovery
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
DEMON: Mining and Monitoring Evolving Data
IEEE Transactions on Knowledge and Data Engineering
A Statistical Theory for Quantitative Association Rules
Journal of Intelligent Information Systems
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Density-Based Mining of Quantitative Association Rules
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
A General Incremental Technique for Maintaining Discovered Association Rules
Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
Improving the Effectiveness of a Web Site with Web Usage Mining
WEBKDD '99 Revised Papers from the International Workshop on Web Usage Analysis and User Profiling
Knowledge discovery from users Web-page navigation
RIDE '97 Proceedings of the 7th International Workshop on Research Issues in Data Engineering (RIDE '97) High Performance Database Management for Large-Scale Applications
A Web page prediction model based on click-stream tree representation of user behavior
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Weighted Association Rule Mining using weighted support and significance framework
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
IncSpan: incremental mining of sequential patterns in large database
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining longest repeating subsequences to predict world wide web surfing
USITS'99 Proceedings of the 2nd conference on USENIX Symposium on Internet Technologies and Systems - Volume 2
Post-analysis of learned rules
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Interpreting the web-mining results by cognitive map and association rule approach
Information Processing and Management: an International Journal
Utility-based association rule mining: A marketing solution for cross-selling
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
This paper considers a problem of finding predictive and useful association rules with a new Web mining algorithm, a streaming association rule (SAR) model. We first adopt a weighted order-dependent scheme (assigning more weights for early visited pages) rather than taking a traditional Boolean scheme (assigning 1 for visited and 0 for non-visited pages). This way, we intend to improve the limited representation of navigation patterns in previous association rule mining (ARM) algorithms. We also note that most traditional association rule models are not scalable because they require multiple scans of all records to re-calibrate a predictive model when there are new updates in original databases. The proposed SAR model takes a ''divide-and-conquer'' approach and requires only single scan of data sets to avoid the curse of dimensionality. Through comparative experiments on a real-world data set, we show that prediction models based on a weighted order-dependent representation are more accurate in predicting the next moves of Web navigators than models based on a Boolean representation. In particular, when combined with several heuristics developed to eliminate redundant association rules, SAR models show a very comparable prediction accuracy while maintaining a small fraction of association rules compared to traditional ARM models. Finally, we quantify and graphically show the significance or contribution of each pages to forming unique rule sets in each database segments.