Algorithms for clustering data
Algorithms for clustering data
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Learning in the presence of concept drift and hidden contexts
Machine Learning
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Tracking Context Changes through Meta-Learning
Machine Learning - Special issue on multistrategy learning
Progressive partial memory learning
Progressive partial memory learning
In search of reliable usage data on the WWW
Selected papers from the sixth international conference on World Wide Web
Machine Learning - Special issue on context sensitivity and concept drift
A framework for measuring changes in data characteristics
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Pruning and summarizing the discovered associations
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Using a knowledge cache for interactive discovery of association rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A statistical theory for quantitative association rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Selecting Examples for Partial Memory Learning
Machine Learning
Machine Learning - Special issue on context sensitivity and concept drift
Efficient Mining of Association Rules in Distributed Databases
IEEE Transactions on Knowledge and Data Engineering
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Using a Hash-Based Method with Transaction Trimming for Mining Association Rules
IEEE Transactions on Knowledge and Data Engineering
Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
A New Approach to Online Generation of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Mining Optimized Association Rules with Categorical and Numeric Attributes
IEEE Transactions on Knowledge and Data Engineering
Finding Localized Associations in Market Basket Data
IEEE Transactions on Knowledge and Data Engineering
Adapting to Drift in Continuous Domains (Extended Abstract)
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Visualizing Association Mining Results through Hierarchical Clusters
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
On the Discovery of Interesting Patterns in Association Rules
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Data mining for path traversal patterns in a web environment
ICDCS '96 Proceedings of the 16th International Conference on Distributed Computing Systems (ICDCS '96)
Batch learning in domains with hidden changes in context
Batch learning in domains with hidden changes in context
Itemset Trees for Targeted Association Querying
IEEE Transactions on Knowledge and Data Engineering
Association mining in time-varying domains
Association mining in time-varying domains
Algorithms for mining frequent itemsets in static and dynamic datasets
Intelligent Data Analysis
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The input of a classical application of association mining is a large set of transactions, each consisting of a list of items a customer has paid for at a supermarket checkout desk. The goal is to identify groups of items ("itemsets") that frequently co-occur in the same shopping carts. This paper focuses on an aspect that has so far received relatively little attention: the composition of the list of frequent itemsets may change in time as the purchasing habits get affected by fashion, season, and introduction of new products. We investigate (1) heuristics for the detection of such changes in time-ordered databases and (2) techniques that update the set of frequent itemsets when the change is detected. As the main performance criterion, we use the accuracy with which our program maintains the current list of frequent itemsets in a time-varying environment.