Clustering Algorithm Based on Time Series Similarity to Web Data Clustering

DOI

10.2991/nceece-15.2016.242How to use a DOI?

Keywords

time series; rough set; similarity; data clustering; Web Recommendation

Abstract

In the view of the information inaccuracy and additional information in the web page recommendation algorithm of multi similar web pages, the data clustering effect is not satisfactory. In this paper, a new method for the image denoising method based on the rough set of Gauss block is proposed. Firstly, based on the information uncertainty, we use rough set theory to improve the traditional probabilistic data clustering model, which is suitable to deal with the problem of information uncertainty. Secondly, to solve the problem that the fixed probability data clustering is used to deal with the problem of web page tag recommendation, the problem is that the new algorithm has higher accuracy and efficiency compared with the new information.

Copyright

© 2016, 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  - Yan Yang
AU  - Hua-Xiong Yao
AU  - Rong Li
PY  - 2015/12
DA  - 2015/12
TI  - Clustering Algorithm Based on Time Series Similarity to Web Data Clustering
BT  - Proceedings of the 2015 4th National Conference on Electrical, Electronics and Computer Engineering
PB  - Atlantis Press
SP  - 1373
EP  - 1377
SN  - 2352-5401
UR  - https://doi.org/10.2991/nceece-15.2016.242
DO  - 10.2991/nceece-15.2016.242
ID  - Yang2015/12
ER  -