Sarasjati, Wendy (2017) Determining prominent research area and expertise from scholarly data using spherical K-Means algorithm and scholar ranking model. Masters thesis, Universiti Teknikal Malaysia Melaka.
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Determining Prominent Research Area And Expertise From Scholarly Data Using Spherical K-Means Algorithm And Scholar Ranking Model - Wendy Sarasjati - 24 Pages.pdf - Submitted Version Download (121kB) |
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Abstract
There are lots of information given through the website or online media nowadays. These include data of research publication such as the data available on scholarly data e.g. Google Scholar. Determine the prominent research area and finding the key players is the motivation of this study. Despite many people may know about the published articles of certain researchers, however there are no information on the research areas of an institute or university where the researchers belong to. Thus, this study will investigate how the prominent research area can be determined by using Spherical K-Means algorithm to cluster the topics. Likewise the proposed expert search approach could determine the key players who are the experts in certain research area. In order to identify the experts, this study identifies the prominent of research area using novel ranking measure i.e. scholar ranking model. This study applies top-down approach in order to solve the problem. This top-down approach initially represents UTeM research areas then followed by the prominent research study. Thus, based on this prominent research study then it comes up with the experts which have related to each field. This study achieves the first objective using spherical K-Means that is determine the prominent topics in UTeM such as Advanced Computing Technology, Telecommunication Research and Communication, Advanced Manufacturing Technology, and Robotic Industrial Automation. Besides, this study also completes the second objective which is identifying the experts based on the prominent research study. The outcome to achieve the second objective is ranking each candidate expert based on the citation by using scholar ranking model.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | Data mining, Web database |
| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Library > Tesis > FTMK |
| Depositing User: | Nor Aini Md. Jali |
| Date Deposited: | 25 Apr 2018 09:23 |
| Last Modified: | 27 Sep 2022 15:41 |
| URI: | http://eprints.utem.edu.my/id/eprint/20761 |
| Statistic Details: | View Download Statistic |
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