OPTICS on Text Data: Experiments and Test Results

Clustering, particularly text clustering, in data mining has been attracting a lot of attention of late. There have been conventional techniques like K-means, which involve parameters that can’t be easily estimated. With the emergence of density-based clustering algorithms which have significant advantages, a lot of attention has been devoted to them. OPTICS [1] is the latest and most sophisticated technique in this direction, and has been shown to be considerably tolerant to value changes in parameters. To the best of our knowledge, this is the first study on the applicability of OPTICS on text data. We perform a variety of experiments towards this end using various feature selection techniques (which,as we show, assume greater significance in the context of density based clustering), quantify our results by way of explanations and list conclusions.