The Research of Grade Prediction Model Based on Improved K-means Algorithm
Authors
Yongguang Zhang, Hua Wang, Hongyang Li
Corresponding Author
Yongguang Zhang
Available Online November 2016.
- DOI
- 10.2991/aiie-16.2016.2How to use a DOI?
- Keywords
- k-means algorithm; grade prediction; similarity measurement
- Abstract
Grades reflect how well you learnt in courses. This paper introduce a model to predict student grade-data with a refined K-means clustering algorithm. K-means clustering algorithm based on the normal distribution is proposed to overcome the flaws that caused by using Euclidean distance algorithm to measure the similarity between objects. Experiment results show that K-means clustering algorithm based on the normal distribution is more accurate than classical K-means clustering algorithm in grade-data prediction.
- Copyright
- © 2016, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Yongguang Zhang AU - Hua Wang AU - Hongyang Li PY - 2016/11 DA - 2016/11 TI - The Research of Grade Prediction Model Based on Improved K-means Algorithm BT - Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016) PB - Atlantis Press SP - 7 EP - 10 SN - 1951-6851 UR - https://doi.org/10.2991/aiie-16.2016.2 DO - 10.2991/aiie-16.2016.2 ID - Zhang2016/11 ER -