Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)

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/).

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Volume Title
Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)
Series
Advances in Intelligent Systems Research
Publication Date
November 2016
ISBN
978-94-6252-271-8
ISSN
1951-6851
DOI
10.2991/aiie-16.2016.2How to use a DOI?
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  -