The Research of Grade Prediction Model Based on Improved K-means Algorithm

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/).

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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  -