Data Mining in Network Engineering—Bayesian Networks for Data Mining

DOI

10.2991/emcs-15.2015.84How to use a DOI?

Keywords

Data mining; Bayesian networks; Network engineering; Bayesian approach; Probability distribution

Abstract

Nowadays,data mining is a hot topic in all sorts of fields. Potential science applications include, Telecommunications companies apply data mining to detect fraudulent network usage. Companies in many areas of business apply data mining to improve their marketing and advertising. Law enforcement uses data mining to detect various financial crimes. Given the well known complexity of Network engineering processes and artifacts, it is perhaps not surprising that data mining can be applied there as well. A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest [1]. When used in conjunction with statistical techniques, the graphical model has several advantages for data modeling. In this paper, discussing methods for constructing Bayesian networks from prior knowledge and summarize Bayesian statistical methods for using data to improve these models. With regard to the latter task, describing methods for learning both the parameters and structure of a Bayesian network.

Copyright

© 2015, 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  - Xiaodan Wang
PY  - 2015/01
DA  - 2015/01
TI  - Data Mining in Network Engineering—Bayesian Networks for Data Mining
BT  - Proceedings of the International Conference on Education, Management, Commerce and Society
PB  - Atlantis Press
SP  - 404
EP  - 409
SN  - 2352-5398
UR  - https://doi.org/10.2991/emcs-15.2015.84
DO  - 10.2991/emcs-15.2015.84
ID  - Wang2015/01
ER  -