Probabilistic algorithm for mining frequent sequences | Zendy

Julija Pragarauskaitė | Zendy; Gintautas Dzemyda | Zendy

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Probabilistic algorithm for mining frequent sequences

Author(s) -

Julija Pragarauskaitė,

Gintautas Dzemyda

Publication year - 2010

Publication title -

lietuvos matematikos rinkinys

Language(s) - English

Resource type - Journals

eISSN - 2335-898X

pISSN - 0132-2818

DOI - 10.15388/lmr.2010.57

Subject(s) - probabilistic logic , computer science , sequence (biology) , data mining , probabilistic analysis of algorithms , sample (material) , algorithm , sequence database , gsp algorithm , probabilistic database , database , artificial intelligence , association rule learning , database theory , relational database , apriori algorithm , biochemistry , chemistry , genetics , chromatography , gene , biology

The subject of the paper is to analyze the problem of the frequency of the subsequences in large volume sequences (texts, databases, etc.). A new algorithm ProMFS for mining frequent sequences is proposed. It is based on the estimated probabilistic-statistical characteristics of the appearance of elements of the sequence and their order. The algorithm builds a new much shorter sequence and makes decisions on the main sequence in accordance with the results of analysis of the shorter one.

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