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
Analyzing the Interestingness of Association Rules from the Temporal Dimension
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Mining Temporal Features in Association Rules
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Monitoring Change in Mining Results
DaWaK '01 Proceedings of the Third International Conference on Data Warehousing and Knowledge Discovery
Temporal evolution and local patterns
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
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Data repositories are constantly evolving and techniques are needed to reveal the dynamic behaviors in the data that might be useful to the user. Existing temporal association rules mining algorithms consider time as another dimension and do not describe the behavior of rules over time. In this work, we introduce the notion of trend fragment to facilitate the analysis of relationships among rules. Two algorithms are proposed to find the relationships among rules. Experiment results on both synthetic and real-world datasets indicate that our approach is scalable and effective.