Novel approaches to clustering, biclustering algorithms based on adaptive resonance theory and intelligent control.

摘要

The problem of clustering is one of the most widely studied area in data mining and machine learning. Adaptive resonance theory (ART), an unsupervised learning clustering algorithm, is a clustering method that can learn arbitrary input patterns in a stable, fast and self-organizing way. This dissertation focuses on unsupervised learning methods, mostly based on variations of ART.;Hierarchical ART clustering is studied by generating a tree of ART units with GPU based parallelization to provide fast and finesse clustering. Experiment results show that the our method achieves significant training speed increase in generating deep ART trees compared with that from non-parallelized version.;In order to handle high dimensional, noisy data more accurately, a hierarchical biclustering ARTMAP (H-BARTMAP) is developed. The nature of biclustering, which considers the correlation of each members in clusters, combined with the concept of hierarchical clustering, provides highly accurate experimental results, especially in bioinformatics data sets.;The third paper focuses on applying the biclustering concept to a supervised learning method, named supervised BARTMAP (S-BARTMAP). Experimental results on high dimensional data sets show that S-BARTMAP is capable of making better predictions compared with those from other math based and machine learning methods.;The final paper focuses on solving the semi-supervised support vector machine (;3VM