dc.contributor.authorWong, Lai Pingen_US
dc.date.accessioned2008-09-17T09:04:50Z
dc.date.accessioned2017-07-23T08:28:26Z
dc.date.available2008-09-17T09:04:50Z
dc.date.available2017-07-23T08:28:26Z
dc.date.copyright2007en_US
dc.date.issued2007
dc.identifier.citationWong, L. P. (2007). Hierarchical clustering using K-Iterations Fast Learning Artificial Neural Networks (KFLANN). Doctoral thesis, Nanyang Technological University, Singapore.
dc.identifier.urihttp://hdl.handle.net/10356/2530
dc.description.abstractHierarchical clustering using hybrid learning model of KFLANN and Multilayer Perceptron with Backpropagation learning algorithm (MLP-BP) is proposed to address high dimensional classification problems. K-Iterations Fast Learning Artificial Neural Network (KFLANN) was enhanced to tackle the sensitivity of clustering against Data Presentation Sequence. Number of cluster is not required prior clustering process for KFLANN clustering algorithm. Data driven scheme is used to define network parameters and only small number of iterations is needed for the algorithm to converge. The KFLANN tends to cumbersome when feature dimensionality is large. HieFLANN and HieFLANN-BP were proposed to avoid this cumbersome. Hierarchical network made up of KFLANN (HieFLANN) was developed to address the limitation of KFLANN in handling large dimensionality problem set. HieFLANN performs clustering and data transformation within a single model. Data transformation adopts canonical covariance concept. HieFLANN only perform classical clustering on a given problem set, thus it lacks of generalization ability. HieFLANN-BP with hybrid learning model as its subunits was build to tackle this issue. It inherits generalization capability from the MLP-BP. Performance of a learning system tends to drop when portion of irrelevant information increases. Feature selection scheme based on purity and relevance (PURE) was proposed to filter irrelevant information.en_US
dc.rightsNanyang Technological Universityen_US
dc.subjectDRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
dc.titleHierarchical clustering using K-Iterations Fast Learning Artificial Neural Networks (KFLANN)en_US
dc.typeThesisen_US
dc.contributor.schoolSchool of Computer Engineeringen_US
dc.contributor.supervisorXu Jian
dc.contributor.supervisorTay Leng Phuan, Alexen_US
dc.description.degreeDOCTOR OF PHILOSOPHY (SCE)en_US


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