Publication Date:
2013-03-30
Description:
Frequent sequence mining is one of the main challenges in data mining and especially in large databases, which consist of millions of records. There is a number of different applications where frequent sequence mining is very important: medicine, finance, internet behavioural data, marketing data, etc. Exact frequent sequence mining methods make multiple passes over the database and if the database is large, then it is a time consuming and expensive task. Approximate methods for frequent sequence mining are faster than exact methods because instead of doing multiple passes over the original database, they analyze a much shorter sample of the original database formed in a specific way. This paper presents Markov Property Based Method (MPBM) – an approximate method for mining frequent sequences based on kth order Markov models, which makes only several passes over the original database. The method has been implemented and evaluated using real-world foreign exchange database and compared to exact and approximate frequent sequent mining algorithms. Content Type Journal Article Pages 87-102 Authors Julija Pragarauskaitė, Institute of Mathematics and Informatics, Vilnius University, Akademijos 4, LT-08663 Vilnius, e-mail: julija.pragarauskaite@gmail.com, gintautas.dzemyda@mii.vu.lt Gintautas Dzemyda, Institute of Mathematics and Informatics, Vilnius University, Akademijos 4, LT-08663 Vilnius, e-mail: julija.pragarauskaite@gmail.com, gintautas.dzemyda@mii.vu.lt Journal Informatica Online ISSN 1822-8844 Print ISSN 0868-4952 Journal Volume Volume 24 Journal Issue Volume 24, Number 1 / 2013
Print ISSN:
0868-4952
Electronic ISSN:
1822-8844
Topics:
Computer Science
