String searching algorithm: Encyclopedia II - String searching algorithm - Other classification

String searching algorithm - Other classification

Other classification approaches are possible. One of the most common uses preprocessing as main criteria.

String searching algorithm - Naïve string search

The simplest and least efficient way to see where one string occurs inside another is to check each place it could be, one by one, to see if it's there. So first we see if there's a copy of the needle in the first few characters of the haystack; if not, we look to see if there's a copy of the needle starting at the second character of the haystack; if not, we look starting at the third character, and so forth. In the normal case, we only have to look at one or two characters for each wrong position to see that it is a wrong position, so in the average case, this takes O(n + m) steps, where n is the length of the haystack and m is the length of the needle; but in the worst case, searching for a string like "aaaab" in a string like "aaaaaaaaab", it takes O(nm) steps.

KMP computes a deterministic finite state automaton that recognizes inputs with the string to search for as a suffix, so it doesn't need to back up. Boyer-Moore starts searching from the end of the needle, so it can usually jump ahead a whole needle-length at each step. Baeza-Yates and Gonnet uses bits in a word to keep track of whether the previous j characters were a prefix of the search string, and is therefore adaptable to fuzzy string searching. The bitap algorithm is an application of Baeza-Yates' approach.

String searching algorithm - Index methods

Faster search algorithms are based on preprocessing of the text. After building an index, for example a suffix tree or suffix array, these algorithms can find pattern quickly, using a binary search of the index.

Adapted from the Wikipedia article "Other classification", under the G.N U Free Docmentation License. Please also see http://en.wikipedia.org/wiki