Election Data Analysis From Clustering Welfare Data

被引：0
作者：
Zhan, Tiffany [1 ]
机构：
[1] USAOT, Pine Bluff, AR 71601 USA
来源：
2021 IEEE 11TH ANNUAL COMPUTING AND COMMUNICATION WORKSHOP AND CONFERENCE (CCWC) | 2021年
关键词：
clustering; welfare data; data analysis;
D O I：
10.1109/CCWC51732.2021.9376049
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Understanding trends in elections based on socioeconomic characteristics is highly important, having implications that permeate throughout society. In this paper, I investigate the relations between various socioeconomic facets of American states and their political alignment, indicated by their choice in the 2016 presidential election through the use of clustering algorithms, K Means and K Medoids. These methods are utilized in order to form clusters in welfare data, and cluster strength will be determined through comparison with the election results.
引用
收藏
页码：475 / 479
页数：5
相关论文
共 13 条
  • [1] [Anonymous], 2018, EVID-BASED COMPL ALT
  • [2] Driver H. E., 1965, QUANTITATIVE EXPRESS
  • [3] T-RECSYS: A Novel Music Recommendation System Using Deep Learning
    Fessahaye, Ferdos
    Perez, Luis
    Zhan, Tiffany
    Zhang, Raymond
    Fossier, Calais
    Markarian, Robyn
    Chiu, Carter
    Zhan, Justin
    Gewali, Laxmi
    Oh, Paul
    [J]. 2019 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE), 2019,
  • [4] Resolving intravoxel white matter structures in the human brain using regularized regression and clustering
    Hart, Andrea
    Smith, Brianna
    Smith, Sean
    Sales, Elijah
    Hernandez-Camargo, Jacqueline
    Garcia, Yarlin Mayor
    Zhan, Felix
    Griswold, Lori
    Dunkelberger, Brian
    Schwob, Michael R.
    Chaudhry, Sharang
    Zhan, Justin
    Gewali, Laxmi
    Oh, Paul
    [J]. JOURNAL OF BIG DATA, 2019, 6 (01)
  • [5] Kim A, 2019, 2019 IEEE 10TH ANNUAL UBIQUITOUS COMPUTING, ELECTRONICS & MOBILE COMMUNICATION CONFERENCE (UEMCON), P1046, DOI 10.1109/UEMCON47517.2019.8992973
  • [6] A general-purpose framework for parallel processing of large-scale LiDAR data
    Li, Zhenlong
    Hodgson, Michael E.
    Li, Wenwen
    [J]. INTERNATIONAL JOURNAL OF DIGITAL EARTH, 2018, 11 (01) : 26 - 47
  • [7] Lower N, 2020, 2020 10TH ANNUAL COMPUTING AND COMMUNICATION WORKSHOP AND CONFERENCE (CCWC), P1001, DOI [10.1109/CCWC47524.2020.9031256, 10.1109/ccwc47524.2020.9031256]
  • [8] Chinese Text Classification Review
    Ma, Yajing
    Li, Yonghong
    Zhang, Xiang
    Wu, Xiaolong
    [J]. 2018 NINTH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY IN MEDICINE AND EDUCATION (ITME 2018), 2018, : 737 - 739
  • [9] SILHOUETTES - A GRAPHICAL AID TO THE INTERPRETATION AND VALIDATION OF CLUSTER-ANALYSIS
    ROUSSEEUW, PJ
    [J]. JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS, 1987, 20 : 53 - 65
  • [10] Tayeb S, 2017, IEEE INT CONF BIG DA, P4522, DOI 10.1109/BigData.2017.8258493
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