2017-03-31T08:16:39Z

2017-03-31

Default License

MinHash

Big Data

Plagiarism Detection in Text Collections

pan_kal_os@hotmail.com

Data Mining

Plagiarism--Prevention

Data mining--Computer programs

Data mining--Programmed instruction

free

[1] Jure Leskovec, Anand Rajaraman, Jeff Ullman , “Stanford University” “Mining of Massive Datasets” Book [online] available at http://www.mmds.org/ [2] From the 1995 Random House Compact Unabridged Dic tionary : use or close imit ation of the language and thoughts of another author and the representation of them as one's own original work qtd. in Stepchyshyn, Vera; Nelson, Robert S. (2007). Library plagiarism policies . Assoc. of College & Resrch Libraries. p. 65. ISBN 0-8389-8416-9 . From the Oxford English Dictionary : the wrongful appropriation or purloining and public a- tion as one's own, of the ideas, or the expression of the ideas... of another qtd. in Lands (1999) [3] Valpy, Francis Edward Jackson (2005) Etymological Dictionary of the Latin Lan- guage , p.345 entry for plagium , quotation: "the crime of kidnapping." [4] Broder, Andrei Z. (1997), "On the resemblance and containment of documents", Com- pression and Complexity of Sequences: Proceedings, Positano, Amalfitan Coast, Salerno, Italy, June 11-13, 1997 (PDF) , -51- [5] Zhao, Kang; Lu, Hongtao; Mei, Jincheng (2014). "Locality Preserving Hashing" . pp. 2874–2880. Tsai, Yi -Hsuan; Yang, Ming- Hsuan (October 2014). "Locality preser v- ing hashing". pp. 2988–2992. [6] https://www.princeton.edu/pr/pub/integrity/pages/plagiarism/ [7] https://en.wikipedia.org/wiki/Jaccard_index

2017-03-24

ABSTRACT .............................................................................................................. IV CONTENTS ............................................................................................................... V 1 INTRODUCTION ................................................................................................. 7 1.1 P LAGIARISM D EFINITION ................................................................................ 7 1.2 P LAGIARISM DETECTION ................................................................................. 7 1.3 W HAT IS OUR P ROBLEM ? ............................................................................... 8 2 SIMILARITY ......................................................................................................... 9 2.1 S IMILARITY OF S ETS .................................................................................... 10 2.2 J ACCARD S IMILARITY ................................................................................... 10 3 SHINGLES ......................................................................................................... 13 3.1 S HINGLING SIZE ........................................................................................... 13 3.2 H ASHIN G SHINGLES ...................................................................................... 14 3.3 P RESERVE SIMILARITY O F SETS WHILE SHRINKI NG THE SPACE ..................... 15 3.4 R EPRESENTATION OF S ETS ......................................................................... 18 4 THE MIN -HASH ALGORITHM ......................................................................... 20 4.1 M IN -H ASHING .............................................................................................. 20 4.2 M IN H ASH AND J ACCARD S IMILARITY C ONNECTION ...................................... 22 4.3 M INHASH S IGNATURES ................................................................................ 23 4.4 C REATING M INHASH S IGNATURES ............................................................... 24 5 LOCALITY SENSITIVE H ASHING .................................................................. 40 5.1 LSH AND D OCUMENTS ................................................................................ 40 5.2 LS H AND M IN -H ASH SIGNATURES ............................................................... 41 5.3 B ANDING T ECHNIQUE ................................................................................... 44 5.4 M ERGING THE T ECHNIQUES ......................................................................... 46 6 CONCLUSIONS ................................................................................................. 49 -vi - 6.1 A PPLICATIONS AND O THER U SES ................................................................. 49 6.2 E VALUATION OF THE A LGORITHM ................................................................. 50 BIBLIOGRAPHY ..................................................................................................... 50 7 APPENDIX ......................................................................................................... 53 7.1 D ATA S AMPLES ............................................................................................ 53 7.2 S OME R ES ULTS ........................................................................................... 55 7.2.1 The Shingling phase ...................................................................... 56 7.2.2 Hashing the Shingles ..................................................................... 57 7.2.3 The MinHash Signature ................................................................ 59 7.2.4 The LSH Banding Technique ....................................................... 60 7.3 SOURCE CODE

papadopo@csd.auth.gr

Gatzianas, Marios

IHU

66