Showing papers on "Approximate string matching published in 1981"
TL;DR: A linear implementation of the optimal universal data compression methods of Lempel and Ziv is described and the main tool is McCreight's algorithm for constructing suffix trees.
Abstract: A linear implementation of the optimal universal data compression methods of Lempel and Ziv is described. The main tool is McCreight's algorithm for constructing suffix trees. Both bounded and unbounded memory are considered.
236 citations
[...]
TL;DR: By using generating function methods, it is shown that the number of distinct correlations of length n is independent of the alphabet size and is of order nlogn.
202 citations
Proceedings Article•
01 Jan 1981TL;DR: An algorithm is designed and analyzed which realizes both asymptotic bounds simultaneously and makes possible a completely general implementation as a Fortran subroutine or even as a six-head finite automaton.
Abstract: Any string-matching algorithm requires at least linear time and a constant number of local storage locations. We design and analyze an algorithm which realizes both asymptotic bounds simultaneously. This can be viewed as completely eliminating the need for the tabulated “failure function” in the linear-time algorithm of Knuth, Morris, and Pratt. It makes possible a completely general implementation as a Fortran subroutine or even as a six-head finite automaton.
172 citations
TL;DR: A sufficient condition for an on-line algorithm to be transformed into a real-time algorithm is given and this condition is used to construct real- time algorithms for various string-matching problems by random access machines and by Turing machines.
Abstract: A sufficient condition for an on-line algorithm to be transformed into a real-time algorithm is given. This condition is used to construct real-time algorithms for various string-matching problems by random access machines and by Turing machines.
69 citations
TL;DR: Using dynamic programming principles, an algorithm is presented which yields X+ without computing individually the distances between every word of H and Y, and it can be shown that it is, in general, computationally less complex than all other existing algorithms which perform the same task.
48 citations
TL;DR: A special case where deletion is the only allowed edition operation is shown to have the longest common subsequence of the strings as its solution.
Abstract: The string merging problem is to determine a merged string from a given set of strings. The distinguishing property of a solution is that the total cost of editing all of the given strings into this solution is minimal. Necessary and sufficient conditions are presented for the case where this solution matches the solution to the string-to-string correction problem. A special case where deletion is the only allowed edition operation is shown to have the longest common subsequence of the strings as its solution.
36 citations