Learning in the presence of concept drift and hidden contexts
Machine Learning
A framework for measuring changes in data characteristics
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Pruning and summarizing the discovered associations
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Small is beautiful: discovering the minimal set of unexpected patterns
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
What Makes Patterns Interesting in Knowledge Discovery Systems
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
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Experiences in building a tool for navigating association rule result sets
ACSW Frontiers '04 Proceedings of the second workshop on Australasian information security, Data Mining and Web Intelligence, and Software Internationalisation - Volume 32
WAM-Miner: in the search of web access motifs from historical web log data
Proceedings of the 14th ACM international conference on Information and knowledge management
Mining and prediction of temporal navigation patterns for personalized services in e-commerce
Proceedings of the 2006 ACM symposium on Applied computing
Mining changing regions from access-constrained snapshots: a cluster-embedded decision tree approach
Journal of Intelligent Information Systems
XML structural delta mining: issues and challenges
Data & Knowledge Engineering - Special issue: ER 2003
Mining Temporal Association Patterns under a Similarity Constraint
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
Mining changing customer segments in dynamic markets
Expert Systems with Applications: An International Journal
Conceptual equivalence for contrast mining in classification learning
Data & Knowledge Engineering
Mining the change of event trends for decision support in environmental scanning
Expert Systems with Applications: An International Journal
On exploiting the power of time in data mining
ACM SIGKDD Explorations Newsletter
A Condensed Representation of Itemsets for Analyzing Their Evolution over Time
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Diverging patterns: discovering significant frequency change dissimilarities in large databases
Proceedings of the 18th ACM conference on Information and knowledge management
Discovering competitive intelligence by mining changes in patent trends
Expert Systems with Applications: An International Journal
Mining changes in association rules: a fuzzy approach
Fuzzy Sets and Systems
Mining dynamic association rules with comments
Knowledge and Information Systems
Discovering association rules change from large databases
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
An integrated approach for mining meta-rules
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Using boolean differences for discovering ill-defined attributes in propositional machine learning
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
Association rule variation with respect to time
Proceedings of the CUBE International Information Technology Conference
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The world around us changes constantly. Knowing what has changed is an important part of our lives. For businesses, recognizing changes is also crucial. It allows businesses to adapt themselves to the changing market needs. In this paper, we study changes of association rules from one time period to another. One approach is to compare the supports and/or confidences of each rule in the two time periods and report the differences. This technique, however, is too simplistic as it tends to report a huge number of rule changes, and many of them are, in fact, simply the snowball effect of a small subset of fundamental changes. Here, we present a technique to highlight the small subset of fundamental changes. A change is fundamental if it cannot be explained by some other changes. The proposed technique has been applied to a number of real-life datasets. Experiments results show that the number of rules whose changes are unexplainable is quite small (about 20% of the total number of changes discovered), and many of these unexplainable changes reflect some fundamental shifts in the application domain.