Applied Constraints on Sequential Pattern Mining with Prefixspan Algorithm

International Journal of Computer Trends and Technology (IJCTT)          
© 2018 by IJCTT Journal
Volume-57 Number-1
Year of Publication : 2018
Authors : Kalpesh A. Kshatriya, Bhavikkumar M. Patel, Hitesh B.Patel


Kalpesh A. Kshatriya, Bhavikkumar M. Patel, Hitesh B.Patel "Applied Constraints on Sequential Pattern Mining with Prefixspan Algorithm". International Journal of Computer Trends and Technology (IJCTT) V57(1):44-50, March 2018. ISSN:2231-2803. Published by Seventh Sense Research Group.

Sequential pattern mining is the process of applying data mining techniques to a sequence database for the purpose of discovering the correlation that exist among an order list of events. Here the PrefixSpan [1] sequential pattern mining algorithm is used to generate sequential patterns from the dataset. After applying any sequential pattern mining algorithm on large dataset there could be a huge number of sequential patterns generated which are very hard to understand and hard to use by the users[2]. Users are often interested in only small subset of such patterns so by inserting several constraints with the sequential pattern mining algorithm we can restrict the algorithm from generating such a huge number of patterns. Here we study constraints like Item, Duration and Length of Transaction with the PrefixSpan algorithm in order to handle the large database. Less number of sequential patterns is generated when we use the PrefixSpan algorithm with Item, Duration and Length of Transaction constraint.

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pattern, mining, PrefixSpan, data