An Efficient Algorithm for Mining Closed Frequent Pattern on Quantitative Databases

Mining frequent patterns is a crucial issue in data mining. Frequent patterns can meet real-world needs, such as mining association rules and serving as input for various artificial intelligence systems. Therefore, research on efficient methods for mining frequent patterns has garnered significant attention recently. However, in many cases, only frequent closed patterns are of interest, which are significantly fewer in number compared to regular, frequent patterns but still enable generating all desired frequent patterns. This paper proposes an algorithm for efficiently mining closed frequent patterns in quantitive databases (FWUCIs). Experimental results on sample datasets demonstrate the significant effectiveness of the proposed method.