Application of An Improved DBSCAN Algorithm in Web Text Mining

DOI

10.2991/ccis-13.2013.92How to use a DOI?

Keywords

Web text mining; DBSCAN; hierarchical clustering; self-adapt

Abstract

This paper studies the characteristics and key technology of Web text mining, and puts forward an improved DBSCAN density clustering algorithm.The algorithm combines the characteristics of hierarchical clustering effectively, it can confirmed class center well,and make the neighborhood parameter r self-adapt to the data sets with different density. To the data sets with different density, it can adjust parameters according to the dense degree. Simulation experiment results verify the proposed algorithm can improve the accuracy in the Web text mining.

Copyright

© 2013, 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  - Ping Xie
AU  - Lin Zhang
AU  - Ying Wang
AU  - Qinqian Li
PY  - 2013/11
DA  - 2013/11
TI  - Application of An Improved DBSCAN Algorithm in Web Text Mining
BT  - Proceedings of the The 1st International Workshop on Cloud Computing and Information Security
PB  - Atlantis Press
SP  - 400
EP  - 403
SN  - 1951-6851
UR  - https://doi.org/10.2991/ccis-13.2013.92
DO  - 10.2991/ccis-13.2013.92
ID  - Xie2013/11
ER  -