Minmax Circular Sector Arc for External Plagiarism’s Heuristic Retrieval stage

Citation data:

Knowledge-Based Systems, ISSN: 0950-7051, Vol: 137, Page: 1-18

Publication Year:
2017
Usage 14
Abstract Views 14
Captures 8
Readers 8
Social Media 51
Shares, Likes & Comments 48
Tweets 3
DOI:
10.1016/j.knosys.2017.08.013
Author(s):
Fellipe Duarte; Danielle Caled; Geraldo Xexéo
Publisher(s):
Elsevier BV
Tags:
Computer Science; Business, Management and Accounting; Decision Sciences
Most Recent Tweet View All Tweets
article description
Heuristic Retrieval (HR) task aims to retrieve a set of documents from which the External Plagiarism detection identifies plagiarized pieces of text. In this context, we present Minmax Circular Sector Arcs ( MinmaxCSA ) algorithms that treats HR task as an approximate k -nearest neighbor search problem. Moreover, MinmaxCSA algorithms aim to retrieve the set of documents with greater amounts of plagiarized fragments, while reducing the amount of time to accomplish the HR task. Our theoretical framework is based on two aspects: (i) a triangular property to encode a range of sketches on a unique value; and (ii) a Circular Sector Arc property which enables (i) to be more accurate. Both properties were proposed for handling high-dimensional spaces, hashing them to a lower number of hash values. Our two MinmaxCSA methods, Minmax Circular Sector Arcs Lower Bound ( CSAL ) and Minmax Circular Sector Arcs Full Bound ( CSA ), achieved Recall levels slightly more imprecise than Minmaxwise hashing in exchange for a better Speedup in document indexing and query extraction and retrieval time in high-dimensional plagiarism-related datasets.