A New Evolving Tree for Text Document Clustering and Visualization

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