Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/14977
Title: Machine learning methods for pattern analysis and clustering
Authors: HE JI
Keywords: Pattern Analysis, Machine Learning, Clustering, Neural Networks, Adaptive Resonance Theory, Adaptive Resonance Theory Under Constraint
Issue Date: 1-Nov-2005
Source: HE JI (2005-11-01). Machine learning methods for pattern analysis and clustering. ScholarBank@NUS Repository.
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.
URI: http://scholarbank.nus.edu.sg/handle/10635/14977
Appears in Collections:Ph.D Theses (Open)

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