Finding Topics in News Web Pages by Parameter-free Clustering

DOI

10.2991/icebi.2010.2How to use a DOI?

Keywords

Topic Detection, Clustering Algorithm, Parameter Free, Similarity Distribution

Abstract

Topic detection is a novel technology which structures news stories into several topics. Present topic detection approaches are mainly based on clustering algorithms such as single pass or agglomerative clustering, and all these algorithms need at least one input parameter. We proposed a novel clustering algorithm which automatically determines the parameters for each corpus. Experimental results show that the parameters derived are close to optimal, and our algorithm has similar accuracy as the UPGMA algorithm which is manually set with optimal parameters. Another advantage of our algorithm is that it runs much faster than the UPGMA algorithm.

Copyright

© 2010, 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  - Ji Xiang
AU  - Neng Gao
AU  - Jiwu Jing
PY  - 2010/12
DA  - 2010/12
TI  - Finding Topics in News Web Pages by Parameter-free Clustering
BT  - Proceedings of the 1st International Conference on E-Business Intelligence (ICEBI 2010)
PB  - Atlantis Press
SP  - 8
EP  - 16
SN  - 1951-6851
UR  - https://doi.org/10.2991/icebi.2010.2
DO  - 10.2991/icebi.2010.2
ID  - Xiang2010/12
ER  -