Efficiently Mining Constrained Subsequence Patterns (original) (raw)
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IJERT-Closed Sequential Pattern Mining using Length Constraint
International Journal of Engineering Research and Technology (IJERT), 2016
https://www.ijert.org/closed-sequential-pattern-mining-using-length-constraint https://www.ijert.org/research/closed-sequential-pattern-mining-using-length-constraint-IJERTV4IS120420.pdf Sequential Pattern Mining is an approach to find sequences that occurs frequently in a dataset. These sequences are later used for predicting occurrence of next event/item in sequences. Sequential Pattern Mining is widely used in Areas like Healthcare, Education, Web Usage Mining, text mining, bioinformatics and telecommunications. Traditional approaches mines frequent closed sequences (sequence that do not have any superset sequence with same support). BIDE is an efficient algorithm for mining closed sequences. These approaches generate large number of sequences many of them are useless. This paper proposed an approach to efficiently mine sequences by incorporating constraint length in algorithms, while mining sequences. Incorporating length constraints reduces number of patterns generated and thus produces less number of sequences. Length constraint is incorporated in an algorithm BIDE which mines frequent closed sequences from multidimensional dataset. In other algorithm, which mines sequences considering time, from dataset, length constraint is added. This also reduced the number of sequences generated.