ITUFP: A fast method for interactive mining of Top-K frequent patterns from uncertain data

Abstract

Top-K Uncertain Frequent Pattern (UFP) mining is an interesting topic in data mining. The existing TUFP algorithm supports static mining of Top-K UFPs; however, in the real world, users need to repeatedly change the K threshold to extract the information according to the requirements of their application. In interactive environments, the TUFP algorithm needs to re-scan the database and create the UP-Lists and CUP-Lists from scratch which is very time-consuming. In this paper, a fast method called ITUFP is proposed for interactive mining of Top-K UFPs. The proposed method uses a new data structure called IMCUP-List to store information of patterns efficiently. It creates the UP-Lists with a single database scan, extracts the patterns by generating IMCUP-Lists, and stores all the lists. When K changes, the proposed algorithm only updates the IMCUP-Lists without having to create the lists from scratch. Accordingly, ITUFP conforms to the ‘‘build once, mine many” principle, where the UP-Lists and IMCUP-Lists are created only once and used in mining with different K values. This is the first study on interactive mining of Top-K UFPs. Extensive experimental results with sparse and dense uncertain data prove that the proposed method is very efficient for interactive mining of Top-K UFPs.