A New Approach to String Pattern Mining with Approximate Match

Abstract

Frequent string mining is the problem of finding frequently appearing strings in a given string database or large text. It has many applications to string data analysis on strings such as texts, word sequences, and genome sequences. The problem becomes difficult if the string patterns are allowed to match approximately, i.e., a fixed number of errors leads to an explosion in the number of small solutions, and a fixed ratio of errors violates the monotonicity that disables hill climbing algorithms, and thus makes searching difficult. There would be also a difficulty on the modeling of the problem; simple mathematical definitions would result explosion of the solutions. To solve this difficulty, we go back to the motivations to find frequent strings, and propose a new method for generating string patterns that appear in the input string many times. In the method, we first compute the similarity between the strings in the database, and enumerate clusters generated by similarity. We then compute representative strings for each cluster, and the representatives are our frequent strings. Further, by taking majority votes, we extend the obtained representatives to obtain long frequent strings. The computational experiments we performed show the efficiency of both our model and algorithm; we were able to find many string patterns appearing many times in the data, and that were long but not particularly numerous. The computation time of our method is practically short, such as 20 minutes even for a genomic sequence of 100 millions of letters.