DNA SEQUENCE DATABASE CLASSIFICATION AND REDUCTION: ROUGH SETS THEORY APPROACH

Abstract

Data classification is a vital task in large scale data mining application. DNA sequences are the basis of life and they encode all the necessary information needed to reproduce life. The size of public DNA sequence databases are growing doubling every year. This situation makes automatic classification and reduction of DNA sequences has become important for effective sequence similarity search problem. A challenge in DNA sequence similarity search is that the sequence record structure does not have any attribute that can be used for implementing classification process. In this paper, by means of filtering process an automaton based exact string matching is employed to generate a special attribute used for DNA sequence database classification and reduction. Rough sets theory provides an indiscernibility relation technique which can be used to classify and reduct the database based on some definition of ‘equivalence’. The generated attribute is used for constructing indiscernibility relation among sequences. With computational implementation, the experiments are executed to investigate the effectiveness of rough sets theory on generating DNA sequence database classification and reduction. Moreover, the experiments will demonstrate that the DNA sequence similarity search performance is significantly improved by using this approach.