| dc.creator |
Moo-Yoo, Seong |
|
| dc.creator |
Zhaparov, Meirambek |
|
| dc.creator |
Serek, Azamat |
|
| dc.creator |
Talasbek, Assem |
|
| dc.creator |
Kim, Yong Kab |
|
| dc.creator |
Jeong, Geun-Ho |
|
| dc.date |
2020-01-01T00:00:00Z |
|
| dc.date.accessioned |
2021-12-03T11:31:37Z |
|
| dc.date.available |
2021-12-03T11:31:37Z |
|
| dc.identifier |
86998a2b-f27c-4e5c-8e2e-0c0c6a57e74d |
|
| dc.identifier |
10.3991/ijet.v15i16.15049 |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/86998a2b-f27c-4e5c-8e2e-0c0c6a57e74d/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/93122 |
|
| dc.description |
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. |
|
| dc.language |
eng |
|
| dc.rights |
info:eu-repo/semantics/closedAccess |
|
| dc.title |
Personality Classification Experiment by Applying k-Means Clustering |
|
| dc.type |
info:eu-repo/semantics/article |
|