Using kohonen maps and singular value decomposition for plagiarism detection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F11%3A86085191" target="_blank" >RIV/61989100:27240/11:86085191 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/CICSyN.2011.25" target="_blank" >http://dx.doi.org/10.1109/CICSyN.2011.25</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CICSyN.2011.25" target="_blank" >10.1109/CICSyN.2011.25</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Using kohonen maps and singular value decomposition for plagiarism detection

Popis výsledku v původním jazyce

Plagiarism has become one area of interest for re-searchers due to its importance, and its fast growing rates. Effective clustering methods and faster search tools for matching and discovering the similarities between documents were the main two areas for the researchers. Many tools and techniques have been developed for plagiarism detection. In this paper we use singular value decomposition for its effective clustering of the documents in-order to reduce search time by creating a new matrix with fewerdimensions used for clustering the original (source) documents, and we use Neural Networks for local matching and comparison between a suspicious document and a source document, Kohonen maps (Self-organizing maps (SOM)) used to visualized and comparisonof the result, in which represent the result as picture that easier to be analyzed. 2011 IEEE.

Název v anglickém jazyce

Using kohonen maps and singular value decomposition for plagiarism detection

Popis výsledku anglicky

Plagiarism has become one area of interest for re-searchers due to its importance, and its fast growing rates. Effective clustering methods and faster search tools for matching and discovering the similarities between documents were the main two areas for the researchers. Many tools and techniques have been developed for plagiarism detection. In this paper we use singular value decomposition for its effective clustering of the documents in-order to reduce search time by creating a new matrix with fewerdimensions used for clustering the original (source) documents, and we use Neural Networks for local matching and comparison between a suspicious document and a source document, Kohonen maps (Self-organizing maps (SOM)) used to visualized and comparisonof the result, in which represent the result as picture that easier to be analyzed. 2011 IEEE.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2011

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings - 3rd International Conference on Computational Intelligence, Communication Systems and Networks, CICSyN 2011

ISBN

978-0-7695-4482-3

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

60-64

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

Bali

Datum konání akce

26. 7. 2011

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—