Mining Frequent Substring Patterns with Ternary Partitioning

The frequent substring pattern mining problem is the problem of enumerating all substrings appearing more frequently than some threshold in a given string. This paper introduces a novel mining algorithm that is faster and requires less working memory than existing algorithms. Moreover, the algorithm generates a data structure that is useful for browsing frequent substring patterns and their contexts in the original text.
My proposed algorithm is divide-and-conquer approach that decomposes the mining task into a set of smaller tasks by using a ternary partitioning technique. After the process of frequent substring mining, the algorithm generates an incomplete suffix array which represents a pruned suffix trie based on the number of occurrences. This incomplete suffix represents the appearance positions of frequent substring patterns compactly so that their contexts can be browsed quickly. Although the average time complexity of this algorithm is O(n log n), a trial performance study shows the proposed algorithm runs faster than the pattern enumeration using a suffix tree.

By: Yuta Tsuboi

Published in: RT0548 in 2007


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