文章基本信息

标题：SPMLS : An Efficient Sequential Pattern Mining Algorithm with candidate Generation and Frequency Testing
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作者：M.Parimala ; S.Sathiyabama
期刊名称：International Journal on Computer Science and Engineering
印刷版ISSN：2229-5631
电子版ISSN：0975-3397
出版年度：2012
卷号：4
期号：04
页码：601-607
出版社：Engg Journals Publications
摘要：Sequential pattern mining is a fundamental and essential field of data mining because of its extensive scope of applications spanning from the forecasting the user shopping patterns, and scientific discoveries. The objective is to discover frequently appeared sequential patterns in given set of sequences. Now-a-days, many studies have contributed to the efficiency of sequential pattern mining algorithms. Most existing algorithms have verified to be effective, however, when mining long frequent sequences in database, these algorithms do not work well. In this paper, we propose an efficient pattern mining algorithm, SPMLS, Sequential Pattern Mining on Long Sequences for mining long sequential patterns in a given database. SPMLS takes up an iterative process of candidate-generation which is followed by frequency-testing in two phases, event-wise and sequence-wise. Event-wise phase presents a new candidate pruning approach which improves the efficiency of the mining process. Sequence-wise phase integrates considerations of intra-event and inter-event constraints. Simulations are carried out on both synthetic and real datasets to evaluate the performance of SPMLS.
关键词：Data Mining; Sequential Patterns; Long Sequences