Analysis of Medical Documents with Text Mining and Association Rule Mining

2019 ◽  
pp. 744-753
Author(s):  
Ruth Reátegui ◽  
Sylvie Ratté
Keyword(s):  
Text Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Medical Documents

scholarly journals Knowledge Discovering on Graphene Green Technology by Text Mining in National R&D Projects in South Korea

2020 ◽  
Vol 12 (23) ◽  
pp. 9857
Author(s):  
Ji Yeon Lee ◽  
Richa Kumari ◽  
Jae Yun Jeong ◽  
Tae-Hyun Kim ◽  
Byeong-Hee Lee
Keyword(s):  
Text Mining ◽  
South Korea ◽  
New Deal ◽  
Association Rule ◽  
Topic Modeling ◽  
Association Rule Mining ◽  
Information Service ◽  
Green Technology ◽  
Rule Mining ◽  
Project Management System

This paper reviews the development of South Korea’s national research and development (R&D) in graphene technology, focusing on projects that have been classified as “green” technology. A total of 826 projects (USD 210 billion) from 2010 to 2019 were collected from the National Science and Technology Information Service (NTIS), which is full-cycle national R&D project management system in South Korea. Then we analyzed its R&D trend by conducting diverse text mining methods including frequency analysis, association rule mining, and topic modeling. The analysis suggests that the number of graphene green technology (GT) R&D projects and the research expenses will show a rising curve again in the incumbent government along with the implementation of the Korean New Deal policy, which integrates the Green New Deal and the Digital New Deal.

Product opportunity identification based on internal capabilities using text mining and association rule mining

2016 ◽  
Vol 105 ◽  
pp. 94-104 ◽  
Author(s):  
Wonchul Seo ◽  
Janghyeok Yoon ◽  
Hyunseok Park ◽  
Byoung-youl Coh ◽  
Jae-Min Lee ◽  
...  
Keyword(s):  
Text Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Opportunity Identification ◽  
Rule Mining ◽  
Internal Capabilities

Prediksi Code Defect Perangkat Lunak Dengan Metode Association Rule Mining dan Cumulative Support Thresholds

2015 ◽  
Vol 6 (2) ◽  
Author(s):  
Rizal Setya Perdana ◽  
Umi Laili Yuhana
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Program Code

Kualitas perangkat lunak merupakan salah satu penelitian pada bidangrekayasa perangkat lunak yang memiliki peranan yang cukup besar dalamterbangunnya sistem perangkat lunak yang berkualitas baik. Prediksi defectperangkat lunak yang disebabkan karena terdapat penyimpangan dari prosesspesifikasi atau sesuatu yang mungkin menyebabkan kegagalan dalam operasionaltelah lebih dari 30 tahun menjadi topik riset penelitian. Makalah ini akandifokuskan pada prediksi defect yang terjadi pada kode program (code defect).Metode penanganan permasalahan defect pada kode program akan memanfaatkanpola-pola kode perangkat lunak yang berpotensi menimbulkan defect pada data setNASA untuk memprediksi defect. Metode yang digunakan dalam pencarian polaadalah memanfaatkan Association Rule Mining dengan Cumulative SupportThresholds yang secara otomatis menghasilkan nilai support dan nilai confidencepaling optimal tanpa membutuhkan masukan dari pengguna. Hasil pengujian darihasil pemrediksian defect kode perangkat lunak secara otomatis memiliki nilaiakurasi 82,35%.

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