Classification model of network users based on optimized LDA and entropy

Author

Liu, Peng ; Liu, Fang ; Dou, Yinan ; Lei, Zhenming

Author_Institution

Sch. of Inf. & Commun. Eng., Beijing Univ. of Posts & Telecommun., Beijing, China

fYear

2009

fDate

6-8 Nov. 2009

Firstpage

149

Lastpage

154

Abstract

The classification of network users is very important in user behavior analysis. The algorithm which was based entropy and latent Dirichlet allocation (LDA) was used in this paper. It is important but difficult to select an appropriate number of topics for a specific dataset. Entropy was first used to solve the problem. A concept named difference-entropy was built to determine the number of topics. Experiments show that the proposed method can achieve performance matching the best of LDA without manually tuning the number of topics.

Keywords

Internet; behavioural sciences computing; entropy; optimisation; entropy; latent Dirichlet allocation; network users classification model; optimized LDA; user behavior analysis; Computer networks; Data processing; Entropy; Inference algorithms; Information analysis; Large-scale systems; Linear discriminant analysis; Machine learning; Machine learning algorithms; Network topology; LDA; classify; entropy; network users;

fLanguage

English

Publisher

ieee

Conference_Titel

Network Infrastructure and Digital Content, 2009. IC-NIDC 2009. IEEE International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-4898-2

Electronic_ISBN

978-1-4244-4900-6

Type

conf

DOI

10.1109/ICNIDC.2009.5360869

Filename

5360869