Title :
An Efficient Subsequences Mining Algorithm
Author_Institution :
Dept. of Comput. Sci., Zhejiang Bus. Technol. Inst., Ningbo, China
Abstract :
As a step forward to analyzing patterns in sequences, we introduce the problem of mining closed repetitive gapped subsequences and propose efficient solutions. Given a database of sequences where each sequence is an ordered list of events, the pattern we would like to mine is called repetitive gapped subsequence. Different from the sequential pattern mining problem, repetitive support captures not only repetitions of a pattern in different sequences but also the repetitions within a sequence. Given a users-specified support threshold min_sup, we study finding the set of all patterns with repetitive support no less than min_sup. To obtain a compact yet complete result set and improve the efficiency, we also study finding closed patterns. Efficient mining algorithms to find the complete set of desired patterns are proposed based on the idea of instance growth. Our performance study on various datasets shows the efficiency of our approach. A case study is also performed to show the utility of our approach.
Keywords :
DNA; biology computing; data mining; pattern clustering; proteins; closed patterns; mining algorithm; repetitive gapped subsequence; repetitive support; Computer science; Credit cards; Data mining; Databases; History; Information resources; Partial response channels; Pattern analysis; Sequences; Tin;
Conference_Titel :
Bioinformatics and Biomedical Engineering , 2009. ICBBE 2009. 3rd International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-2901-1
Electronic_ISBN :
978-1-4244-2902-8
DOI :
10.1109/ICBBE.2009.5162317
