Data Clustering via Dimension Reduction and Algorithm Aggregation

Abstract

We focus on the problem of clustering large textual data sets. We present 3 well-known clustering algorithms and suggest enhancements involving dimension reduction. We propose a novel method of algorithm aggregation that allows us to use many clustering algorithms at once to arrive on a single solution. This method helps stave off the inconsistency inherent in most clustering algorithms as they are applied to various data sets. We implement our algorithms on several large benchmark data sets.