C4.5: programs for machine learning
C4.5: programs for machine learning
Knowledge-based artificial neural networks
Artificial Intelligence
Symbolic Representation of Neural Networks
Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Finding Interesting Patterns Using User Expectations
IEEE Transactions on Knowledge and Data Engineering
Overcoming Process Delays with Decision Tree Induction
IEEE Expert: Intelligent Systems and Their Applications
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Rerepresenting and restructuring domain theories: a constructive induction approach
Journal of Artificial Intelligence Research
Flexibly exploiting prior knowledge in empirical learning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Post-analysis of learned rules
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Argument based generalization of MODLEM rule induction algorithm
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
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In this article, we discuss how previously discovered knowledge can be used in future knowledge-discovery processes. This is an important topic as in real-life environments data collected in databases change, and patterns mined in the past may no longer be valid. In such situations, the users always want to know what data and patterns have changed and how they have changed. The ability to mine interesting knowledge and/or changes based on previously discovered knowledge is crucial. This article reviews some main techniques related to this topic.