Open AccessFinding Frequent Patterns in Biological Networks
Author(s) -
Rahul Rane,
Praveen Kaushik
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i4.6.20482
Subject(s) - traverse , computer science , biological network , task (project management) , sampling (signal processing) , algorithm , data mining , cluster (spacecraft) , artificial intelligence , biological data , complex network , pattern recognition (psychology) , mathematics , bioinformatics , economics , computer vision , biology , programming language , geography , world wide web , management , geodesy , filter (signal processing) , combinatorics
Frequent patterns help to discover the structural behavior of the complex biological network. The networks which show same global structure may have different local structure. The patterns are the actual fingerprints of the network. The pattern frequency and its significance carries very important functionality to classify and cluster the biological network. The sheer number of patterns get generated while traversing the network. Counting these patterns and determining their frequencies in the large biological network is very challenging and computationally expensive task. Previously proposed methods are bound by the size of patterns and networks size. By using approximation method like sampling, we proposed an algorithm. The results show the proposed algorithm is faster than existing algorithms. Also, the error rate is minimum, which makes the proposed method more reliable.