Interesting patterns extraction using prior knowledge
Résumé
One important challenge in data mining is to extract interesting knowledge and useful information for expert users. Since data mining algorithms extracts a huge quantity of patterns it is therefore necessary to filter out those patterns using various measures. This paper presents IMAK, a part-way interestingness measure between objective and subjective measure, which evaluates patterns considering expert knowledge. Our main contribution is to improve interesting patterns extraction using relationships defined into an ontology.
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