On Discovering Feasible Periodic Patterns in Large Database
Xiao Luo, Hua Yuan, Qian Luo · 2013
In real applications, there are two problems for the periodic patterns mining task: finding the frequent pattern(s) and determining their periodicity. In this paper, we propose a new method to investigate the periodic patterns form common frequent patterns. First, all the candidates patterns are generated by general frequent pattern mining algorithm. Then, for each pattern, all the time (order) attributes are extracted form its support records. Finally, all these time (order) attributes are partitioned into suitable n periods to obtain the feasible periodicity. To this end, two new parameters of per and fea are introduced to measure the periodicity and feasibility of the candidate patterns. The experiment results show that the method can be used to explore feasible periodic patterns efficiently and find some interesting patterns in business database.