Interesting Patterns Extraction Using Prior Knowledge

Abstract. 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.

• Type: Conference
• Year: 2006
• Where: DIS
• Authors: Laurent Brisson
• Keywords: Data Mining, Data Mining Algorithms, DIS 2006, Interesting Patterns Extraction, Theoretical Computer Science