PMC:1698483 / 29464-30282 JSONTXT

For repeated structured motif identification problem, the frequency closure property that "all the subsequences of a frequent sequence must be frequent", doesn't hold any more since the frequency of a pattern can exceed the frequency of its sub-patterns. [22] introduces an closure-like property which can help prune the patterns without missing the frequent patterns. The two algorithms proposed in [22] can extract within one sequence all frequent patterns of length no greater than a length threshold, which can be either manually specified or automatically determined. However, this method requires that all the gap ranges [li, ui], between adjacent symbols in the structured motif be the same, i.e., [li, ui] = [l, u] for all i ∈ [1, k - 1]. Moreover, approximate matches are not allowed for the structured motif.