caa a22 4500 606159797 CHVBK 20210128100626.0 cr unu---uuuuu 210128e20150501xx s 000 0 eng 10.1007/s00521-014-1792-9 doi (NATIONALLICENCE)springer-10.1007/s00521-014-1792-9 Text clustering using VSM with feature clusters [Elektronische Daten] [Cao Qimin, Guo Qiao, Wang Yongliang, Wu Xianghua] Representation of documents is the basis of clustering systems. In addition, non-contiguous phrases appear more and more frequent in the text in the Web 2.0 age, and these phrases can affect the result of text clustering. In order to improve the quality of text clustering, this paper proposed a feature cluster-based vector space model (FC-VSM) which used the text feature clusters co-occurrence matrix to represent document and proposed to identify non-contiguous phrases in the text preprocessing stage. Our method can reduce dimension of features compared with the traditional VSM-based model. It identified non-contiguous phrases, used distributed representation of features, and implements feature clusters. Despite their simplicity, our methods are surprisingly effective and can improve the accuracy of clustering significantly which is shown in experimental results. The Natural Computing Applications Forum, 2014 Text clustering nationallicence Feature clusters nationallicence Distributed representation nationallicence FC-VSM nationallicence Non-contiguous phrases nationallicence Qimin Cao School of Automation, Beijing Institute of Technology, 100081, Beijing, China aut Qiao Guo School of Automation, Beijing Institute of Technology, 100081, Beijing, China aut Yongliang Wang School of Automation, Beijing Institute of Technology, 100081, Beijing, China aut Xianghua Wu School of Automation, Beijing Institute of Technology, 100081, Beijing, China aut Neural Computing and Applications Springer London 26/4(2015-05-01), 995-1003 0941-0643 26:4<995 2015 26 521 https://doi.org/10.1007/s00521-014-1792-9 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1792-9 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Qimin Cao School of Automation, Beijing Institute of Technology, 100081, Beijing, China aut NATIONALLICENCE 700 1- Qiao Guo School of Automation, Beijing Institute of Technology, 100081, Beijing, China aut NATIONALLICENCE 700 1- Yongliang Wang School of Automation, Beijing Institute of Technology, 100081, Beijing, China aut NATIONALLICENCE 700 1- Xianghua Wu School of Automation, Beijing Institute of Technology, 100081, Beijing, China aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/4(2015-05-01), 995-1003 0941-0643 26:4<995 2015 26 521