Title :
A Fast Biological Data Mining Algorithm Based on Embedded Frequent Subtree
Author_Institution :
Dept. of Inf. Technol., Nanjing Xiaozhuang Coll., Nanjing, China
Abstract :
In this paper, we present a fast biological data mining algorithm named IRTM based on embedded frequent subtree. We also advance a string encoding method for representing the trees, a scope-list for extending all substrings and some pruning rules which can further reduce the computational time and space cost. Experimental results show that IRTM algorithm can achieve significantly performance improvement over previous works.
Keywords :
biology computing; data mining; string matching; trees (mathematics); IRTM; biological data mining; embedded frequent subtree; string encoding; Algorithm design and analysis; Biological information theory; Data mining; Databases; Encoding; RNA; Biological data;
Conference_Titel :
Multimedia Information Networking and Security (MINES), 2010 International Conference on
Conference_Location :
Nanjing, Jiangsu
Print_ISBN :
978-1-4244-8626-7
Electronic_ISBN :
978-0-7695-4258-4
DOI :
10.1109/MINES.2010.152
