A Comparative Study between Single-Pass Algorithm and K-means Algorithm in Web Topic Detection

DOI

10.2991/icaicte-14.2014.41How to use a DOI?

Keywords

Text data, Clustering algorithm, Topic detection.

Abstract

As with the extensive application of the Internet, the explosive growth of information and unprecedented enthusiasm of users, the monitoring and management of Web content is becoming more and more imminent. Although traditional Single-Pass algorithm and K-means algorithm each has shortcomings, they are widely used in clustering analysis because of their relatively simple prin-ciples and fast computing speed. This paper firstly describes the overall flow of the entire topic of detection, then we make a comparison between Single-Pass algorithm and K-means algorithm. In order to verify the comparison, finally, an experiment is designed. The result shows that Single-Pass algorithm is better than K-means algorithm in Web topic detection.

Copyright

© 2014, 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  - Ting Xi
AU  - Jufang Li
PY  - 2014/08
DA  - 2014/08
TI  - A Comparative Study between Single-Pass Algorithm and K-means Algorithm in Web Topic Detection
BT  - Proceedings of the 2014 International Conference on Advanced ICT (ICAICTE 2014)
PB  - Atlantis Press
SP  - 190
EP  - 195
SN  - 2352-538X
UR  - https://doi.org/10.2991/icaicte-14.2014.41
DO  - 10.2991/icaicte-14.2014.41
ID  - Xi2014/08
ER  -