Probabilistic Measures for Interestingness of Deviations - A Survey

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Provided by: Academy & Industry Research Collaboration Center
Topic: Networking
Format: PDF
Association rule mining has long being plagued with the problem of finding meaningful, actionable knowledge from the large set of rules. In this age of data deluge with modern computing capabilities, the authors gather, distribute, and store information in vast amounts from diverse data sources. With such data profusion, the core knowledge discovery problem becomes efficient data retrieval rather than simply finding heaps of information. The most common approach is to employ measures of rule interestingness to filter the results of the association rule generation process.
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