A new filtration method and a hybrid strategy for approximate string matching

In this paper, we propose a new filtration algorithm, as well as a hybrid filtration strategy, to efficiently solve the approximate string matching problem (also called the k-difference problem), which aims to find all the positions i’s in a given text such that there exists a substring of the text ending at position i whose edit distance from a given pattern is less than or equal to a given error bound k. Our experimental results on simulated datasets of DNA sequences show that when compared with other filtration algorithms, our filtration algorithm has better performance on the efficiency to filter out those positions of the text at which the pattern does not occur approximately. Moreover, our hybrid filtration strategy further improves the effectiveness of our filtration algorithm.