Enhancing an evolving tree-based text document visualization model with fuzzy c-Means clustering Chang, Wui Lee, Tay, Kai Meng and Lim, Chee Peng 2013, Enhancing an evolving tree-based text document visualization model with fuzzy c-Means clustering, in FUZZ-IEEE 2013 : Proceedings of the IEEE International Conference on Fuzzy Systems, IEEE, Piscataway, N.J., pp. 1-6. Attached Files Name Description MIMEType Size Downloads Title Enhancing an evolving tree-based text document visualization model with fuzzy c-Means clustering Author(s) Chang, Wui Lee Tay, Kai Meng Lim, Chee Peng orcid.org/0000-0003-4191-9083 Conference name Fuzzy Systems. IEEE International Conference (2013 : Hyderabad, India) Conference location Hyderabad, India Conference dates 7-10 Jul. 2013 Title of proceedings FUZZ-IEEE 2013 : Proceedings of the IEEE International Conference on Fuzzy Systems Editor(s) [Unknown] Publication date 2013 Conference series IEEE International Conference on Fuzzy Systems Start page 1 End page 6 Total pages 6 Publisher IEEE Place of publication Piscataway, N.J. Keyword(s) evolving tree text document clustering visualization online learning fuzzy c-means Summary An improved evolving model, i.e., Evolving Tree (ETree) with Fuzzy c-Means (FCM), is proposed for undertaking text document visualization problems in this study. ETree forms a hierarchical tree structure in which nodes (i.e., trunks) are allowed to grow and split into child nodes (i.e., leaves), and each node represents a cluster of documents. However, ETree adopts a relatively simple approach to split its nodes. Thus, FCM is adopted as an alternative to perform node splitting in ETree. An experimental study using articles from a flagship conference of Universiti Malaysia Sarawak (UNIMAS), i.e., Engineering Conference (ENCON), is conducted. The experimental results are analyzed and discussed, and the outcome shows that the proposed ETree-FCM model is effective for undertaking text document clustering and visualization problems. Language eng Field of Research 089999 Information and Computing Sciences not elsewhere classified Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category E1 Full written paper - refereed Copyright notice ©2013, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30057147 Document type: Conference Paper Collections: Institute for Intelligent Systems Research and Innovation (IISRI) GTP Research   Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Wed, 23 Oct 2013, 09:59:41 EST Wed, 23 Oct 2013, 14:27:54 EST Thu, 31 Oct 2013, 15:05:49 EST Tue, 03 Dec 2013, 15:29:38 EST Wed, 04 Dec 2013, 07:38:54 EST Wed, 04 Dec 2013, 07:40:23 EST Wed, 21 Oct 2015, 14:51:57 EST Sat, 22 Jan 2022, 01:32:04 EST Filtered Full Citation counts:   Cited 2 times in TR Web of Science Cited 4 times in Scopus Search Google Scholar Access Statistics: 490 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Wed, 23 Oct 2013, 09:59:41 EST