Favorable support threshold recommendation for multidimensional association mining using user preference ontology

The classical algorithms for mining association rule require the user to specify a support threshold to determine if an itemset is frequent or not. Unfortunately, the setting of support threshold is subjective without clear standard and has great influence on the mining results. In this paper we propose an intelligent minimum support suggestion framework with the help of the user preference ontology. The user preference ontology maintains the frequently used mining queries extracted from the mining log. The system finds the most similar queries to the user´s mining intension, aggregates them and obtains the favorable support range for the user to refer. In this paper we describe briefly the construction of the user preference ontology and focus on the methodology for query similarity comparison.