Abstract
In this paper, we focus on frequent pattern mining using non Boolean similarity functions. Several properties and propositions that allow pruning the search space of frequent similar patterns, are proposed. Based on these properties, an algorithm for mining frequent similar patterns using non Boolean similarity functions is also introduced. We evaluate the quality of the frequent similar patterns computed by our algorithm by means of a supervised classifier based on frequent patterns.
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Rodríguez-González, A.Y., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Ruiz-Shulcloper, J. (2010). Using Non Boolean Similarity Functions for Frequent Similar Pattern Mining. In: Farzindar, A., Kešelj, V. (eds) Advances in Artificial Intelligence. Canadian AI 2010. Lecture Notes in Computer Science(), vol 6085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13059-5_50
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DOI: https://doi.org/10.1007/978-3-642-13059-5_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13058-8
Online ISBN: 978-3-642-13059-5
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