title: Frequent patterns mining in multiple biological sequences.
abstract: Existing algorithms for mining frequent patterns in multiple biosequences may generate multiple projected databases and short candidate patterns, which can increase computation time and memory requirement. In order to overcome such shortcomings, we propose a fast and efficient algorithm for mining frequent patterns in multiple biological sequences (MSPM). We first present the concept of a primary pattern, which can be extended to form larger patterns in the sequence. To detect frequent primary patterns, a prefix tree is constructed. Based on this prefix tree, a pattern-extending approach is also presented to mine frequent patterns without producing a large number of irrelevant candidate patterns. The experimental results show that the MSPM algorithm can achieve not only faster speed, but also higher quality results as compared with other methods.
authors.affiliation: College of Information Engineering, Yangzhou University, Yangzhou, Jiangsu 225009, China; National Key Lab of Novel Software Tech, Nanjing University, Nanjing 210093, China. Electronic address: yzulchen@gmail.com.
authors.forename: Ling
authors.initials: L
authors.lastname: Chen
authors.forename: Wei
authors.initials: W
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods; D006801:Humans; D009369:Neoplasms / Q000235:genetics / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012044:Regression Analysis
keywords: Biological sequence; Frequent pattern mining; Prefix tree; Primary pattern