A new evolving tree-based model with local re-learning for document clustering and visualization Chang, Wui Lee, Tay, Kai Meng and Lim, Chee Peng 2017, A new evolving tree-based model with local re-learning for document clustering and visualization, Neural processing letters, pp. 1-31, doi: 10.1007/s11063-017-9597-3. Attached Files Name Description MIMEType Size Downloads Title A new evolving tree-based model with local re-learning for document clustering and visualization Author(s) Chang, Wui Lee Tay, Kai Meng Lim, Chee Peng orcid.org/0000-0003-4191-9083 Journal name Neural processing letters Start page 1 End page 31 Total pages 31 Publisher Springer Place of publication Berlin, Germany Publication date 2017-02-06 ISSN 1370-4621 1573-773X Keyword(s) Evolving tree Textual documents Clustering Visualization Local re-learning Summary The Evolving tree (ETree) is a hierarchical clustering and visualization model that allows the number of clusters to grow and evolve with new data samples in an online learning manner. While many hierarchical clustering models are available in the literature, ETree stands out because of its visualization capability. It is an enhancement of the Self-Organizing Map, a famous and useful clustering and visualization model. ETree organises the trained data samples in the form of a tree structure for better presentation and visualization especially for high-dimensional data samples. Even though ETree has been used in a number of applications, its use in textual document clustering and visualization is limited. In this paper, ETree is modified and deployed as a useful model for undertaking textual documents clustering and visualization problems. We introduce a new local re-learning procedure that allows the tree structure to grow and adapt to new features, i.e., new words from new textual documents. The performance of the proposed ETree model is evaluated with two (one benchmark and one real) document data sets. A number of key aspects of the proposed ETree model, which include its topology representation, learning time, as well as recall and precision rates, are evaluated. The results show that the proposed local re-learning procedure is useful for handling increasing number of features incrementally. In summary, this study contributes towards a modified ETree model and its use in a new domain, i.e., textual document clustering and visualization. Notes In Press Language eng DOI 10.1007/s11063-017-9597-3 Field of Research 0801 Artificial Intelligence And Image Processing 1702 Cognitive Science Socio Economic Objective 0 Not Applicable HERDC Research category C1 Refereed article in a scholarly journal Copyright notice ©2017, Springer Persistent URL http://hdl.handle.net/10536/DRO/DU:30093346 Document type: Journal Article Collection: Centre for Intelligent Systems Research Related Links Link Description Connect to published version Go to link with your DU access privileges   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Wed, 05 Apr 2017, 11:41:43 EST Thu, 06 Apr 2017, 05:07:12 EST Fri, 21 Apr 2017, 13:53:28 EST Tue, 06 Jun 2017, 06:04:57 EST Wed, 07 Jun 2017, 11:37:45 EST Wed, 07 Jun 2017, 11:39:57 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 9 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Wed, 05 Apr 2017, 11:41:43 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.