Discovering informative patterns and data cleaning
Advances in knowledge discovery and data mining
Fast discovery of association rules
Advances in knowledge discovery and data mining
A Multistrategy Approach to Relational Knowledge Discovery inDatabases
Machine Learning - Special issue on multistrategy learning
A sequential sampling algorithm for a general class of utility criteria
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Principles of data mining
Concept Formation and Knowledge Revision
Concept Formation and Knowledge Revision
Instance Selection and Construction for Data Mining
Instance Selection and Construction for Data Mining
Data Mining and Knowledge Discovery
An Algorithm for Multi-relational Discovery of Subgroups
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
An iterative hypothesis-testing strategy for pattern discovery
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Features for learning local patterns in time-stamped data
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
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Most valid rules that are learned from very large and high dimensional data sets are not interesting, but are already known to the users. The dominant model of the overall data set may well suppress the interesting local patterns. The search for interesting local patterns can be implemented by a two step learning approach which first acquires the global models before it focuses on the rest in order to detect local patterns. In this paper, three sets of interesting instances are distinguished. For these sets, the hypothesis space is enlarged in order to characterize local patterns in a second learning step.