Gaussian Naive Bayesian Data Classification Model Based on Clustering Algorithm

DOI

10.2991/masta-19.2019.67How to use a DOI?

Keywords

Clustering algorithm, Naive bayesian algorithm, Classification model

Abstract

A gaussian naive bayesian data classification model based on clustering algorithm was proposed for fast recognition and classification of unknown continuous data containing a large number of non-priori knowledge. Firstly, the unknown data were extracted from the representative samples according to the information entropy measure for clustering to generate class labels. Then, the mapping relationship between data and class labels was established by using the gaussian naive bayes algorithm, and the classification model was obtained through training. Simulation results show that this unsupervised analysis process has a good classification effect on new data.

Copyright

© 2019, 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  - Zeng-jun Bi
AU  - Yao-quan Han
AU  - Cai-quan Huang
AU  - Min Wang
PY  - 2019/07
DA  - 2019/07
TI  - Gaussian Naive Bayesian Data Classification Model Based on Clustering Algorithm
BT  - Proceedings of the 2019 International Conference on Modeling, Analysis, Simulation Technologies and Applications (MASTA 2019)
PB  - Atlantis Press
SP  - 396
EP  - 400
SN  - 1951-6851
UR  - https://doi.org/10.2991/masta-19.2019.67
DO  - 10.2991/masta-19.2019.67
ID  - Bi2019/07
ER  -