A combinatorial optimization for influential factor analysis: A case study of political preference in Korea

Sung Bum Yun, Sanghyun Yoon, Joon Heo

Research output: Contribution to journalArticlepeer-review

Abstract

Finding influential factors from given clustering result is a typical data science problem. Genetic Algorithm based method is proposed to derive influential factors and its performance is compared with two conventional methods, Classification and Regression Tree (CART) and Chi-Squared Automatic Interaction Detection (CHAID), by using Dunn’s index measure. To extract the influential factors of preference towards political parties in South Korea, the vote result of 18th presidential election and ‘Demographic’, ‘Health and Welfare’, ‘Economic’ and ‘Business’ related data were used. Based on the analysis, reverse engineering was implemented. Implementation of reverse engineering based approach for influential factor analysis can provide new set of influential variables which can present new insight towards the data mining field.

Original languageEnglish
Pages (from-to)415-422
Number of pages8
JournalJournal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
Volume35
Issue number5
DOIs
Publication statusPublished - 2017 Oct

Bibliographical note

Funding Information:
This research, ‘Geospatial Big Data Management, Analysis and Service Platform Technology Development’, was supported by the MOLIT(The Ministry of Land, Infrastructure and Transport), Korea, under the national spatial information research program supervised by the KAIA(Korea Agency for Infrastructure Technology Advan cement)”(17NSIP-B081011-04)

All Science Journal Classification (ASJC) codes

  • Earth and Planetary Sciences(all)

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