Journal ArticleDOI
Classes of cost functions for string edit distance
TLDR
It is shown that cost functions having the same ratio of the sum of the insertion and deletion costs divided by the substitution cost yield the same minimum cost sequences of edit operations, which leads to a partitioning of the universe of cost functions into equivalence classes.Abstract:
Finding a sequence of edit operations that transforms one string of symbols into another with the minimum cost is a well-known problem. The minimum cost, or edit distance, is a widely used measure of the similarity of two strings. An important parameter of this problem is the cost function, which specifies the cost of each insertion, deletion, and substitution. We show that cost functions having the same ratio of the sum of the insertion and deletion costs divided by the substitution cost yield the same minimum cost sequences of edit operations. This leads to a partitioning of the universe of cost functions into equivalence classes. Also, we show the relationship between a particular set of cost functions and the longest common subsequence of the input strings.read more
Citations
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Journal ArticleDOI
On a relation between graph edit distance and maximum common subgraph
TL;DR: In this paper a particular cost function for graph edit distance is introduced, and it is shown that under this cost functiongraph edit distance computation is equivalent to the maximum common subgraph problem.
Journal ArticleDOI
Error correcting graph matching: on the influence of the underlying cost function
TL;DR: It is shown that, for a given cost function, there are an infinite number of other cost functions that lead, for any given pair of graphs, to the same optimal error correcting matching.
Book
Data Mining in Time Series Databases
Horst Bunke,Abraham Kandel +1 more
TL;DR: A Survey of Recent Methods for Efficient Retrieval of Similar Time Sequences and Change Detection in Classification Models of Data Mining.
Proceedings ArticleDOI
Quadratic Conditional Lower Bounds for String Problems and Dynamic Time Warping
Karl Bringmann,Marvin Künnemann +1 more
TL;DR: In this article, it was shown that these measures do not have strongly sub quadratic time algorithms, i.e., no algorithm with running time O(n 2 ) for any a#x03B5; > 0, unless the Strong Exponential Time Hypothesis fails.
Journal ArticleDOI
A matching algorithm for measuring the structural similarity between an XML document and a DTD and its applications
TL;DR: The matching algorithm is exploited for the classification of XML documents against a set of DTDs, the evolution of the DTD structure, the evaluation of structural queries, the selective dissemination ofxml documents, and the protection of XML document contents.
References
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Journal ArticleDOI
The String-to-String Correction Problem
TL;DR: An algorithm is presented which solves the string-to-string correction problem in time proportional to the product of the lengths of the two strings.
Book
Time Warps, String Edits, and Macromolecules: The Theory and Practice of Sequence Comparison
David Sankoff,Joseph B. Kruskal +1 more
TL;DR: In this paper, a mudflap assembly for use with a dump vehicle having dual tires at the rear end thereof and including a pair of flexible flap sections one of which is supported by a rigid member adjacent the dual tires and the other is located above and to the rear of the rigid member and is secured at its upper end to the dump body.
Journal ArticleDOI
An O ( ND ) difference algorithm and its variations
TL;DR: A simpleO(ND) time and space algorithm is developed whereN is the sum of the lengths of A andB andD is the size of the minimum edit script forA andB, and the algorithm performs well when differences are small and is consequently fast in typical applications.
Journal ArticleDOI
Algorithms for approximate string matching
TL;DR: An improved algorithm that works in time and in space O and algorithms that can be used in conjunction with extended edit operation sets, including, for example, transposition of adjacent characters.
Book ChapterDOI
Algorithms for finding patterns in strings
TL;DR: This chapter discusses the algorithms for solving string-matching problems that have proven useful for text-editing and text-processing applications and several innovative, theoretically interesting algorithms have been devised that run significantly faster than the obvious brute-force method.