A New Evolving Tree for Text Document Clustering and Visualization

Wui, Lee Chang and Kai, Meng Tay and Chee, Peng Lim (2013) A New Evolving Tree for Text Document Clustering and Visualization. In: A New Evolving Tree for Text Document Clustering and Visualization. Advances in Intelligent Systems and Computing . Springer International Publishing, pp. 141-151. ISBN 978-3-319-00930-8

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Abstract

The Self-Organizing Map (SOM) is a popular neural network model for clustering and visualization problems. However, it suffers from two major limitations, viz., (1) it does not support online learning; and (2) the map size has to be pre-determined and this can potentially lead to many “trial-and-error” runs before arriving at an optimal map size. Thus, an evolving model, i.e., the Evolving Tree (ETree), is used as an alternative to the SOM for undertaking a text document clustering problem in this study. ETree forms a hierarchical (tree) structure in which nodes are allowed to grow, and each leaf node represents a cluster of documents. An experimental study using articles from a flagship conference of Universiti Malaysia Sarawak (UNIMAS), i.e., the Engineering Conference (ENCON), is conducted. The experimental results are analyzed and discussed, and the outcome shows a new application of ETree in text document clustering and visualization.

Item Type: Book Section
Uncontrolled Keywords: Evolving tree, text document clustering, online learning, technology,research, Universiti Malaysia Sarawak, Unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education
Subjects: T Technology > T Technology (General)
Divisions: Academic Faculties, Institutes and Centres > Faculty of Engineering
Depositing User: Karen Kornalius
Date Deposited: 29 Oct 2014 04:12
Last Modified: 27 Jul 2015 06:46
URI: http://ir.unimas.my/id/eprint/5237

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