期刊名称：International Journal of Computer Science & Technology
印刷版ISSN：2229-4333
电子版ISSN：0976-8491
出版年度：2014
卷号：5
期号：4
页码：94-97
语种：English
出版社：Ayushmaan Technologies
摘要：Sequential pattern mining is the mining of frequentlyoccurring ordered events or subsequences as patterns. An example of a sequentialpattern is “Customers who buy a Canon digital camera are likely to buy an HP colorprinter within a month.”Many kinds of Sequential patterns can bemined fromdifferent kinds of Sequencedata sets. Sequential dataset corresponds to the contents of a single database table, or a single statistical data matrix. Existing sequence mining algorithms mostly focus on mining for subsequences. However, a large class of applications, such as biological DNA and protein motif mining, require efficient mining of “approximate” patterns that are contiguous. The fewexisting algorithms that can be applied to find such contiguous approximate pattern mining have drawbacks like poor scalability, lack of guarantees in finding the pattern, and difficulty in adapting to other applications.In this system, we present a new algorithm called SPRINT to find Sequence Pattern and Subsequence pattern efficiently and Eliminating Problem of Poor Scalability lack of Guaranties in finding Pattern.SPRINT is a Decision tree based Parallelized Algorithm.It is also accurate, as it always finds the patterns parallel if it exists. Using both real and synthetic data sets, we demonstrate that SPRINT is fast, scalable, and outperforms existing SPRINT algorithms on a variety of performance metrics.In addition, based on SPRINT, we also address a more general problem, named extended structured motif extraction, which allows mining frequent combinations of motifs under relaxed constraints. We propose to compare SPRINT with FLAME, which is Suffix tree based Algorithm. We proposed to Evaluate these algorithm using various bench mark data sets.
关键词：Motif;Sequence Mining;Suffix Tree;Decision Tree
