Approximate weighted frequent pattern mining with/without noisy environments

In data mining area, weighted frequent pattern mining has been suggested to find important frequent patterns by considering the weights of patterns. More extensions with weight constraints have been proposed such as mining weighted association rules, weighted sequential patterns, weighted closed patterns, frequent patterns with dynamic weights, weighted graphs, and weighted sub-trees or sub structures. In previous approaches of weighted frequent pattern mining, weighted supports of patterns were exactly matched to prune weighted infrequent patterns. However, in the noisy environment, the small change in weights or supports of items affects the result sets seriously. This may make the weighted frequent patterns less useful in the noisy environment. In this paper, we propose the robust concept of mining approximate weighted frequent patterns. Based on the framework of weight based pattern mining, an approximate factor is defined to relax the requirement for exact equality between weighted supports of patterns and a minimum threshold. After that, we address the concept of mining approximate weighted frequent patterns to find important patterns with/without the noisy data. We analyze characteristics of approximate weighted frequent patterns and run extensive performance tests.