Quasilinear-Time Search and Comparison for Sequential Data

WSEAS Transactions on Information Science and Applications

Print ISSN: 1790-0832
E-ISSN: 2224-3402

Volume 14, 2017

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of WSEAS Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.

Quasilinear-Time Search and Comparison for Sequential Data

AUTHORS: Ilhan Karić, Zanin Vejzović

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ABSTRACT: This paper proposes a new algorithm for the evaluation of similarity between two sequences in quasilinear time. It describes the theoretical, practical and implementational aspects of the algorithm. The proposed method is a new approach dedicated to the computation of sequential similarity in contrast to other methods like the Jaccard Index which although designed for the computation of similarity of sets have been frequently used on sequences. The method is generalizable and applicable to any form of sequential data of a finite alphabet (binary files, DNA sequences, natural language etc.)

KEYWORDS: Sequence Similarity, Comparison, Contextual Similarity, Quasilinear-Time Complexity

REFERENCES:

[1] X1. Konrad Rieck, Pavel Laskov, “LinearTime Computation of Similarity Measures for Sequential Data”, Journal of Machine Learning Research 9 (2008) 23-48 pp. 1

[2] S. T. Piantadosi, “Zipf’s word frequency law in natural language: A critical review and future directions”, Psychonomic Bulletin & Review, vol. 21, 2014, pp. 1112-1130

WSEAS Transactions on Information Science and Applications, ISSN / E-ISSN: 1790-0832 / 2224-3402, Volume 14, 2017, Art. #4, pp. 26-30

Copyright © 2017 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0

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