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A Text Mining Algorithm Optimising the Determination of Relevant Studies

In this paper, we develop a text mining algorithm that influences the identification of relevant literature studies. The algorithm consists of three processes, detection process; preparation process; and mining process. The detection process includes the determination of document language and abstra...

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Main Authors: Khashfeh, M., Mahmoud, M.A., Ahmad, M.S.
Format: Conference Paper
Language:English
Published: 2019
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spelling my.uniten.dspace-117572020-07-07T08:50:09Z A Text Mining Algorithm Optimising the Determination of Relevant Studies Khashfeh, M. Mahmoud, M.A. Ahmad, M.S. In this paper, we develop a text mining algorithm that influences the identification of relevant literature studies. The algorithm consists of three processes, detection process; preparation process; and mining process. The detection process includes the determination of document language and abstract and keywords. The Preparation includes the processes, split content to paragraphs; paragraph length determination; converting text to lower case; text typography factor; content tokenization, removing stop words. Finally, the mining includes the processes, regular expression; normalization; grouping and computing frequency. The proposed algorithm would be useful in providing an alternative means of searching highly relevant content from large databases. © 2018 IEEE. 2019-03-06T07:36:39Z 2019-03-06T07:36:39Z 2018 Conference Paper 10.1109/ISAMSR.2018.8540553 en International Symposium on Agents, Multi-Agent Systems and Robotics 2018, ISAMSR 2018 19 November 2018, Article number 8540553 2018 International Symposium on Agents, Multi-Agent Systems and Robotics, ISAMSR 2018; The Everly PutrajayaPutrajaya; Malaysia; 27 August 2018 through ; Category numberCFP18C71-ART; Code 143006
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
language English
description In this paper, we develop a text mining algorithm that influences the identification of relevant literature studies. The algorithm consists of three processes, detection process; preparation process; and mining process. The detection process includes the determination of document language and abstract and keywords. The Preparation includes the processes, split content to paragraphs; paragraph length determination; converting text to lower case; text typography factor; content tokenization, removing stop words. Finally, the mining includes the processes, regular expression; normalization; grouping and computing frequency. The proposed algorithm would be useful in providing an alternative means of searching highly relevant content from large databases. © 2018 IEEE.
format Conference Paper
author Khashfeh, M.
Mahmoud, M.A.
Ahmad, M.S.
spellingShingle Khashfeh, M.
Mahmoud, M.A.
Ahmad, M.S.
A Text Mining Algorithm Optimising the Determination of Relevant Studies
author_facet Khashfeh, M.
Mahmoud, M.A.
Ahmad, M.S.
author_sort Khashfeh, M.
title A Text Mining Algorithm Optimising the Determination of Relevant Studies
title_short A Text Mining Algorithm Optimising the Determination of Relevant Studies
title_full A Text Mining Algorithm Optimising the Determination of Relevant Studies
title_fullStr A Text Mining Algorithm Optimising the Determination of Relevant Studies
title_full_unstemmed A Text Mining Algorithm Optimising the Determination of Relevant Studies
title_sort text mining algorithm optimising the determination of relevant studies
publishDate 2019
_version_ 1672614169059262464
score 13.160551

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