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The string spelled by the edges from the root to such a node is a longest repeated substring. The problem of finding the longest substring with at least k {\displaystyle k} occurrences can be solved by first preprocessing the tree to count the number of leaf descendants for each internal node, and then finding the deepest node with at least k ...
The longest common substrings of a set of strings can be found by building a generalized suffix tree for the strings, and then finding the deepest internal nodes which have leaf nodes from all the strings in the subtree below it. The figure on the right is the suffix tree for the strings "ABAB", "BABA" and "ABBA", padded with unique string ...
A naive implementation would compute the largest common subsequence of all the strings in the set in (). [6] A generalized suffix array can be utilized to find the longest previous factor array, a concept central to text compression techniques and in the detection of motifs and repeats [7]
The longest repeated substring problem for a string of length can be solved in () time using both the suffix array and the LCP array. It is sufficient to perform a linear scan through the LCP array in order to find its maximum value v m a x {\displaystyle v_{max}} and the corresponding index i {\displaystyle i} where v m a x {\displaystyle v ...
A string-searching algorithm, sometimes called string-matching algorithm, is an algorithm that searches a body of text for portions that match by pattern. A basic example of string searching is when the pattern and the searched text are arrays of elements of an alphabet ( finite set ) Σ.
An alternative to building a generalized suffix tree is to concatenate the strings, and build a regular suffix tree or suffix array for the resulting string. When hits are evaluated after a search, global positions are mapped into documents and local positions with some algorithm and/or data structure, such as a binary search in the starting ...
For instance a 32 byte needle ending in "z" searching through a 255 byte haystack which does not have a 'z' byte in it would take up to 224 byte comparisons. The best case is the same as for the Boyer–Moore string-search algorithm in big O notation , although the constant overhead of initialization and for each loop is less.
After computing E(i, j) for all i and j, we can easily find a solution to the original problem: it is the substring for which E(m, j) is minimal (m being the length of the pattern P.) Computing E ( m , j ) is very similar to computing the edit distance between two strings.