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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.

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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 PengORCID iD for 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
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