String parsing-based similarity detection

Author

Yang, Jia ; Speidel, Ulrich

Author_Institution

Dept. of Comput. Sci., Auckland Univ., New Zealand

fYear

2005

fDate

29 Aug.-1 Sept. 2005

Abstract

This paper compares the similarity-detection abilities of two string parsing algorithms from the Lempel-Ziv family and the T-decomposition algorithm proposed by Titchener against the Hamming and Levenshtein measures. Our results show that LZ and T-decomposition based measures work in a wider range of contexts. We also argue that T-decomposition based measures represent a good compromise between accuracy and time complexity.

Keywords

Hamming codes; computational complexity; data compression; program compilers; Hamming measure; Lempel-Ziv family; Levenshtein measure; T-decomposition algorithm; context wider range; similarity-detection ability; string parsing algorithms; Area measurement; Automata; Compression algorithms; Computer science; Data compression; Data mining; History; Length measurement; Production; Time measurement;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Theory Workshop, 2005 IEEE

Print_ISBN

0-7803-9480-1

Type

conf

DOI

10.1109/ITW.2005.1531901

Filename

1531901

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2407944