Please use this identifier to cite or link to this item:
https://scholarbank.nus.edu.sg/handle/10635/14977
DC Field | Value | |
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dc.title | Machine learning methods for pattern analysis and clustering | |
dc.contributor.author | HE JI | |
dc.date.accessioned | 2010-04-08T10:48:49Z | |
dc.date.available | 2010-04-08T10:48:49Z | |
dc.date.issued | 2005-11-01 | |
dc.identifier.citation | HE JI (2005-11-01). Machine learning methods for pattern analysis and clustering. ScholarBank@NUS Repository. | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/14977 | |
dc.description.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. | |
dc.language.iso | en | |
dc.subject | Pattern Analysis, Machine Learning, Clustering, Neural Networks, Adaptive Resonance Theory, Adaptive Resonance Theory Under Constraint | |
dc.type | Thesis | |
dc.contributor.department | COMPUTER SCIENCE | |
dc.contributor.supervisor | TAN CHEW LIM | |
dc.description.degree | Ph.D | |
dc.description.degreeconferred | DOCTOR OF PHILOSOPHY | |
dc.identifier.isiut | NOT_IN_WOS | |
Appears in Collections: | Ph.D Theses (Open) |
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he04thesis.pdf | 1.27 MB | Adobe PDF | OPEN | None | View/Download |
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