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Mining Sequential Pattern By Prefix-Projected Pattern Growth For Protein Sequences

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dc.contributor.author Phyu, Mya Kyar
dc.contributor.author San, Khaing Moe
dc.date.accessioned 2019-07-29T08:18:31Z
dc.date.available 2019-07-29T08:18:31Z
dc.date.issued 2009-12-30
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/1469
dc.description.abstract Data mining involves the use of sophisticated data analysis tools to discover previously unknown, valid patterns and relationships in large data sets. Sequential pattern mining is to find all frequent sequential pattern with a user specified minimum support threshold. PrefixSpan is a pattern growth method, particularly popular in biomedical fields. PrefixSpan is the most promosing of pattern growth method and is based on recursively constructing the pattern and the search to projected database. At each step, algorithm looks for the frequent sequences with prefix  in the corresponding projected database. In prefixspan algorithm, no candidate sequence needs to be generated. The search space is reduced at each step allowing for better performance, in the presence of small support threshold. In this paper, the patterns of protein sequences can be discovered to analyse the structure of amino acids which are building blocks of protein sequences by using prefixspan approach. en_US
dc.language.iso en en_US
dc.publisher Fourth Local Conference on Parallel and Soft Computing en_US
dc.title Mining Sequential Pattern By Prefix-Projected Pattern Growth For Protein Sequences en_US
dc.type Article en_US

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