Exploration of Document Collections with Self-Organizing Maps: A Novel Approach to Similarity Representation

Classification is one of the central issues in any system dealing with text data. The need for effective approaches is dramatically increased nowadays due to the advent of massive digital libraries containing free-form documents. What we are looking for are powerful methods for the exploration of such libraries whereby the detection of similarities between the various text documents is the overall goal. In other words, methods that may be used to gain insight in the inherent structure of the various items contained in a text archive are needed. In this paper we demonstrate the applicability of self-organizing maps, a neural network model adhering to the unsupervised learning paradigm, for the task of text document clustering. In order to improve the representation of the result we present an extension to the basic learning rule that captures the movement of the various weight vectors in a two-dimensional output space for convenient visual inspection. The result of the extended training algorithm allows intuitive analysis of the similarities inherent in the input data and most important, intuitive recognition of cluster boundaries.

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