Edit Distance Function
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An Edit Distance Function is a distance function whose Metric Space is based on the number of Edit Operations required to convert one Object into another Object.
- AKA: Edit Distance Measure.
- Context:
- It can be the Minimum number of Edit Operations.
- It can be:
- It can be a Weighted Edit Distance Function.
- …
- Example(s):
- See: Clustering Algorithm, Edit Distance Algorithm.
References
2015
- (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/edit_distance Retrieved:2015-8-17.
- In computer science, edit distance is a way of quantifying how dissimilar two strings (e.g., words) are to one another by counting the minimum number of operations required to transform one string into the other. Edit distances find applications in natural language processing, where automatic spelling correction can determine candidate corrections for a misspelled word by selecting words from a dictionary that have a low distance to the word in question. In bioinformatics, it can be used to quantify the similarity of macromolecules such as DNA, which can be viewed as strings of the letters A, C, G and T.
Several definitions of edit distance exist, using different sets of string operations. One of the most common variants is called Levenshtein distance, named after the Soviet Russian computer scientist Vladimir Levenshtein. In this version, the allowed operations are the removal or insertion of a single character, or the substitution of one character for another. Levenshtein distance may also simply be called "edit distance", although several variants exist.
- In computer science, edit distance is a way of quantifying how dissimilar two strings (e.g., words) are to one another by counting the minimum number of operations required to transform one string into the other. Edit distances find applications in natural language processing, where automatic spelling correction can determine candidate corrections for a misspelled word by selecting words from a dictionary that have a low distance to the word in question. In bioinformatics, it can be used to quantify the similarity of macromolecules such as DNA, which can be viewed as strings of the letters A, C, G and T.
2013
- (Augsten & Böhlen, 2013) ⇒ Nikolaus Augsten, and Michael H Böhlen. (2013). “Similarity Joins in Relational Database Systems.” In: Synthesis Lectures on Data Management Journal, 5(5). doi:10.2200/S00544ED1V01Y201310DTM038
- QUOTE: Edit-based similarity measures express the difference between two objects by the number of basic edit operations that are required to transform one object into the other.