Evaluating quality of text clustering with ART1

Self-organizing large amounts of textual data in accordance to some topics structure is an increasingly important application of clustering. Adaptive resonance theory (ART) neural networks possess several interesting properties that make them appealing in this area. 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.