On the quality of ART1 text clustering.

Abstract

There is a large and continually growing quantity of electronic text available, which contain essential human and organization knowledge. An important research endeavor is to study and develop better ways to access this knowledge. Text clustering is a popular approach to automatically organize textual document collections by topics to help users find the information they need. Adaptive Resonance Theory (ART) neural networks possess several interesting properties that make them appealing in the area of text clustering. Although ART has been used in several research works as a text clustering tool, the level of quality of the resulting document clusters has not been clearly established yet. In this paper, we present experimental results with binary ART that address this issue by determining how close clustering quality is to an upper bound on clustering quality.