Paper Info
Title
A Deep CFS Model for Text Clustering
Abstract
With the fast development of the Internet technology, the court text information is collected from various fields at an unprecedented speed, such as Weibo and Wechat. This big court text information of high volume poses a vast challenge for the judge making reasonable decisions based on the vast cases. To cluster the reasonable assistant cases from the vast cases, we propose a deep CFS model for the text clustering, which can cluster the court text effectively, in this paper. In the proposed model, a robust deep text feature extractor is designed to improve the cluster accuracy, in which an ensemble of deep learning models are used to learn the deep features of the text. Furthermore, the CFS algorithm is conducted on the extracted deep text features, to discover the non-spherical clusters with the automatic find of the cluster centers. Finally, the proposed deep cluster model is evaluated on two typical datasets and the results show it can perform better than compared models in terms of the cluster accuracy.
Year
DOI
Venue
2018
10.1109/Cybermatics_2018.2018.00054
2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)
Keywords
Field
DocType
Feature extraction,Clustering algorithms,Standards,Computational modeling,Neural networks,Training,Deep learning
Data mining,Cluster (physics),Computer science,Document clustering,Feature extraction,Extractor,Artificial intelligence,Deep learning,Cluster analysis,Artificial neural network,The Internet
Conference
ISBN
Citations 
PageRank 
978-1-5386-7975-3
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Bin Lv1153.03
Weiliang Hou200.34
Guo-hua Liu310234.53
Jing Gao4216.58
Xu Yuan56124.92
Peng Li61003.33
Zhikui Chen769266.76