期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2015
卷号：73
期号：3
出版社：Journal of Theoretical and Applied
摘要：An increasing number of efficient methods have been proposed to mine frequent patterns from uncertain data obtained from real applications such as social networks and life-sciences. Since these data are constantly being updated, needs of the users are changed and they adjust a new minimum support threshold to find new and proper frequent patterns. Obviously, finding the new frequent patterns by running the algorithm from scratch is very costly especially when the database is very large. In this paper, an efficient tree called UDFP-tree is proposed for interactive mining from uncertain data. The proposed tree aims to construct the mining model separated from the mining process. The experimental results show that by using UDFP-tree, there is no need to reconstruct the mining model when user changes the minimum support threshold.
