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  • 标题:Personality Classification Experiment by Applying k-Means Clustering
  • 本地全文:下载
  • 作者:Assem Talasbek ; Azamat Serek ; Meirambek Zhaparov
  • 期刊名称:International Journal of Emerging Technologies in Learning (iJET)
  • 印刷版ISSN:1863-0383
  • 出版年度:2020
  • 卷号:15
  • 期号:16
  • 页码:162-177
  • DOI:10.3991/ijet.v15i16.15049
  • 出版社:Kassel University Press
  • 摘要:This paper describes personality classification experiment by applying k-means clustering machine learning algorithms. Several previous studies have been attempted to predict personality types of human beings automatically by using various machine learning algorithms. However, only few of them have obtained good accuracy results. To classify a person into personality types, we used Jungian Type Inventory. Our method consists of three parts: data collection, data preparation, and hyper-parameter tuning. Our testing results showed that the k-means model has 107 inertia value, which is a good number for an unsupervised learning model as an interim result. With the result, we divided the data into 16 clusters, which can be considered as personality types. We continue this research with analysis of large data to be collected in the future.
  • 关键词:personality types;machine learning;Jungian Type Inventory;k-means clustering on personality test.
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