Efficient strategies for tough aggregate constraint-based sequential pattern mining
Information Sciences: an International Journal
Real-time acquisition of buyer behaviour data: the smart shop floor scenario
BIRTE'06 Proceedings of the 1st international conference on Business intelligence for the real-time enterprises
Sequential pattern mining -- approaches and algorithms
ACM Computing Surveys (CSUR)
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In this paper, we propose an efficient scalable algorithm for mining Maximal Sequential Patterns using Sampling (MSPS). The MSPS algorithm reduces much more search space than other algorithms because both the subsequence infrequency based pruning and the supersequence frequency based pruning are applied. In MSPS, sampling technique is used to identify long frequent sequences earlier, instead of enumerating all their subsequences. We propose how to adjust the user-specified minimum support level for mining a sample of the database to achieve better performance. This method makes sampling more efficient when the minimum support is small. A signature technique is utilized for the subsequence infrequency based pruning when the seed set of frequent sequences for the candidate generation is too big to be loaded into memory. A prefix tree structure is developed to count the candidate sequences of different sizes during the database scanning, and it also facilitates the customer sequence trimming. Our experiments showed MSPS has very good performance and better scalability than other algorithms.