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Algorithm for discovery of sequential patterns in big data and application on price of brent crude oil

T. Ruzgas, J. Arnastauskaite, I. Juskaite

Abstract



Frequent sequence mining in large population is important for economics, engineering, genetics and many other databases analysis. Exact algorithms are designed to search for frequent sequence, they repeatedly re-selects the entire population. If the population is large, the search is slow or requires very large computer resources. The paper proposes a new probabilistic frequencies sequence finding algorithm which analyze a randomized sample of the primary population concluded in a specific way. Based on this analysis, statistical inferences are made about frequent sequence in the primary population. This algorithm is not accurate, but it runs much faster than the exact algorithms and is suitable for exploratory statistical analysis. Probabilities of errors are estimated by statistical methods. The probabilistic algorithm can be combined with the exact algorithms of frequent sequences search.

Keywords


Generalized sequential pattern algorithm, probabilistic algorithm, probability of errors.

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