Reusing Translated Terms to Expand a Multilingual Thesaurus
AMTA '98 Proceedings of the Third Conference of the Association for Machine Translation in the Americas on Machine Translation and the Information Soup
Guiding knowledge discovery through interactive data mining
Managing data mining technologies in organizations
A survey of Knowledge Discovery and Data Mining process models
The Knowledge Engineering Review
Expert Systems with Applications: An International Journal
Evaluation of rule interestingness measures in medical knowledge discovery in databases
Artificial Intelligence in Medicine
Behavioural Proximity Discovery: an adaptive approach for root cause analysis
International Journal of Business Intelligence and Data Mining
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We introduce a methodology for knowledge discovery in databases (KDD) where one first discovers large collections of patterns at once, and then performs interactively retrieves subsets of the collection of patterns. The proposed methodology suits such KDD formalisms as association and episode rules, where large collections of potentially interesting rules can be found efficiently.We present methods that support interactive exploration of large collections of rules. With these methods the user can flexibly specify the focus of interest, and also iteratively refine it.We have implemented our methodology in the TASA system which discovers patterns in telecommunication alarm databases. In this paper, we give concrete examples of how to use frequent patterns in the construction of alarm correlation expert systems.