An Effective Unsupervised Feature Selection Method for Text Clustering

2005 ◽  
Vol 42 (3) ◽  
pp. 381 ◽  
Author(s):  
Tao Liu
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Text Clustering ◽  
Selection Method ◽  
Unsupervised Feature Selection

scholarly journals Two-stage Unsupervised Feature Selection Method Oriented to Manufacturing Procedural Data

2019 ◽  
Vol 55 (17) ◽  
pp. 133
Author(s):  
ZHANG Jie ◽  
SHENG Xia ◽  
ZHANG Peng ◽  
QIN Wei ◽  
ZHAO Xinming
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Two Stage ◽  
Unsupervised Feature Selection

scholarly journals Distance Variance Score: An Efficient Feature Selection Method in Text Classification

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Heyong Wang ◽  
Ming Hong
Keyword(s):  
Feature Selection ◽  
Text Mining ◽  
Text Classification ◽  
Web Applications ◽  
Rapid Development ◽  
Feature Selection Method ◽  
Selection Method ◽  
Text Documents ◽  
Unsupervised Feature Selection ◽  
Classification Feature

With the rapid development of web applications such as social network, a large amount of electric text data is accumulated and available on the Internet, which causes increasing interests in text mining. Text classification is one of the most important subfields of text mining. In fact, text documents are often represented as a high-dimensional sparse document term matrix (DTM) before classification. Feature selection is essential and vital for text classification due to high dimensionality and sparsity of DTM. An efficient feature selection method is capable of both reducing dimensions of DTM and selecting discriminative features for text classification. Laplacian Score (LS) is one of the unsupervised feature selection methods and it has been successfully used in areas such as face recognition. However, LS is unable to select discriminative features for text classification and to effectively reduce the sparsity of DTM. To improve it, this paper proposes an unsupervised feature selection method named Distance Variance Score (DVS). DVS uses feature distance contribution (a ratio) to rank the importance of features for text documents so as to select discriminative features. Experimental results indicate that DVS is able to select discriminative features and reduce the sparsity of DTM. Thus, it is much more efficient than LS.

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