DNA-miner: a system prototype for mining DNA sequences
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Efficient discovery of error-tolerant frequent itemsets in high dimensions
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Parallel Algorithms for Discovery of Association Rules
Data Mining and Knowledge Discovery
Finding Localized Associations in Market Basket Data
IEEE Transactions on Knowledge and 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
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
DEMON: Mining and Monitoring Evolving Data
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
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Mining user profiles is a crucial task for Web usage mining, and can be accomplished by mining frequent patterns. However, in the Web usage domain, sessions tend to be very sparse, and mining the right user profiles tends to be difficult. Either too few or too many profiles tend to be mined, partly because of problems in fixing support thresholds and intolerant matching. Also, in the Web usage mining domain, there is often a need for post-processing and validation of the results of mining. In this paper, we use criterion guided optimization to mine localized and error-tolerant transaction patterns, instead of using exact counting based method, and explore the effect of different post-processing options on their quality. Experiments with real Web transaction data are presented.