Machine learning methods for pattern analysis and clustering

Abstract

Pattern analysis has received intensive research interests in the past decades. This thesis targets efficient cluster analysis of high dimensional and large scale data with user's intuitive prior knowledge. A novel neural architecture named Adaptive Resonance Theory Under Constraint (ART-C) is proposed. The algorithm is subsequently applied to the real-life clustering problems on the gene expression domain and the text document domain. The algorithm has shown significantly higher efficiency over other algorithms in the same family. A set of evaluation paradigms are studied and applied to evaluate the efficacy of the clustering algorithms, with which the clustering quality of ART-C is shown to be reasonably comparable to those of existing algorithms.