Efficient discovery of optimal word-association patterns in large text databases

We study efficient discovery of proximity word-association patterns, defined by a sequence of strings and a proximity gap, from a collection of texts with the positive and the negative labels. We present an algorithm that finds alld-stringsk-proximity word-association patterns that maximize the number of texts whose matching agree with their labels. It runs in expected time complexityO(kd−1n logdn) and spaceO(kd−1n) with the total lengthn of texts, if texts are uniformly random strings. We also show that the problem to find one of the best word-association patterns with arbitrarily many strings in MAX SNP-hard.