Association Rule Mining based on Ontological Relational Weights
N. Radhika, K. R. Vidya · 2012
Data mining has emerged to address the problem of transforming data into useful knowledge. On account of enormous of rules that can be produced by data mining algorithms, knowledge validation is one of the most problematic steps in an association rule discovery process. in this paper we propose a new WARM (weighted association rule mining) approach to prune and filter discovered rules based on the Ontological relational weights. We propose to use ontologies in order to improve the integration of user knowledge. An interesting real-life example and experimental results on different types of data are given; to reduce the numbers of rules to several dozen are less.