Title :
The Maximal Frequent Pattern mining of DNA sequence
Author :
Bai, Shuang ; Bai, Si-Xue
Author_Institution :
Dept. of Comput. Sci. & Technol., Nanchang Univ., Nanchang, China
Abstract :
The DNA sequence data is one of the basic and important data among biological data. The DNA sequence pattern mining has got wide attention and rapid development. Traditional algorithms for the sequential pattern mining may generate lots of redundant patterns when dealing with the DNA sequence. The maximal frequent pattern is preferable to express the function and structure of the DNA sequence. Base on the characteristics of the DNA sequence, the author develops the joined maximal pattern segments algorithm-JMPS, for the maximal frequent patterns mining of the DNA sequence. First, the maximal frequent pattern segments base on adjacent generated. Then, longer maximal frequent pattern can be obtained by combining the above segments, at the same time deleting the nonmaximal patterns. The algorithm can deal with the DNA sequence data efficiently.
Keywords :
DNA; bioinformatics; data mining; molecular biophysics; DNA sequence; biological data mining; joined maximal pattern segments; maximal frequent pattern; pattern mining; Bioinformatics; Biology; DNA; Data mining; Databases; Electronic mail; Genomics; Humans; Itemsets; Sequences; Maximum Frequent Pattern; data mining; the DNA sequence;
Conference_Titel :
Granular Computing, 2009, GRC '09. IEEE International Conference on
Conference_Location :
Nanchang
Print_ISBN :
978-1-4244-4830-2
DOI :
10.1109/GRC.2009.5255169
