Stochastic Algorithm for Mining Frequent Subsequences
Journal Title: Jaunųjų mokslininkų darbai - Year 2011, Vol 33, Issue 4
Abstract
The article gives an overview of stochastic frequent subsequence mining, which separates different length of subsequences randomly when a database is being scanned. The distribution of the length of subsequences depends on a geometric law with parameter p and the distribution of the distance between chosen subsequences also depends on a geometric law with parameter q. The designed algorithm was tested using computer modeling based on methods of statistical hypothesis testing, probability confidence limits, likelihood functions and Monte Carlo for finding frequent subsequences. This algorithm is approximate but allows us to combine two important criteria i.e. time and accuracy, respectively choosing values of parameters p and q. The algorithm gives statistical conclusions about frequent subsequences using analysis of random subsequences. Therefore, the designed stochastic frequent subsequence mining algorithm can be used for mining frequent subsequences in large databases
Authors and Affiliations
Loreta Savulionienė, Leonidas Sakalauskas
Keywords
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