Mining Method of Frequent Patterns Based on Temporal Constraints
DU Yi1,3, LU DeTang1,2, LI DaoLun1,2, WEI WuZhou1,2
1.Institute of Engineering and Science Software, University of Science and Technology of China, Hefei 230027 2.Key Laboratory of Software in Computing and Communication of Anhui Province, Hefei 230027 3.School of Computer and Information, Shanghai Second Polytechnic University, Shanghai 201209
Abstract：Temporal data is a kind of useful information. Temporal attributes in data can be used to find some potential change rules of data and predict the possible tendency. An algorithm, Temporal Frequent Pattern mining algorithm (TemFP) is presented. According to the existing function of temporal query, a Double B+tree for storing time attributes of frequent patterns is described. Using a temporal frequent pattern tree including the Double B+tree, the queries of temporal rules, which defined by users, could be realized rapidly. Experimental results demonstrate that the proposed algorithm is efficient and scalable.
杜奕，卢德唐，李道伦，卫五洲. 时态约束下的频繁模式挖掘算法*[J]. 模式识别与人工智能, 2007, 20(4): 538-544.
DU Yi , LU DeTang , LI DaoLun , WEI WuZhou. Mining Method of Frequent Patterns Based on Temporal Constraints. , 2007, 20(4): 538-544.
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