A hybrid artificial neural network model for data visualisation, classification, and clustering
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Date
2006
Authors
Teh, Chee Siong
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Abstract
In this thesis, the research of a hybrid Artificial Neural Network (ANN) model that is able to produce a topology-preserving map, which is akin to the theoretical explanation of the brain map, for data visualisation, classification, and clustering is presented. The proposed hybrid ANN model integrates the Self-Organising Map (SOM) and the kernel-based Maximum Entropy learning rule (kMER) into a unified framework, and is termed as SOM-kMER. A series of empirical studies comprising benchmark and real-world problems is employed to evaluate the effectiveness of SOM-kMER. The experimental results demonstrate that SOM-kMER is able to achieve a faster convergence rate when compared with kMER, and to produce visualisation with fewer dead units when compared with SOM. It is also able to form an equiprobabilistic map at the end of its learning process. This research has also proposed a variant of SOM-kMER, i.e., probabilistic SOM-kMER (pSOM-kMER) for data classification. The pSOM-kMER model is able to operate in a probabilistic environment and to implement the principles of statistical decision theory in undertaking classification problems. In addition to performing classification, a distinctive feature of pSOM-kMER is its ability to generate visualisation for the underlying data structures. Performance evaluation using benchmark datasets has shown that the results of pSOM-kMER compare favourably with those from a number of machine learning systems. Based on SOM-kMER, this research has further expanded from data classification to data clustering in tackling problems using unlabelled data samples. A new lattice disentangling monitoring algorithm is coupled with SOM-kMER for density-based clustering. The empirical results show that SOM-kMER with the new lattice disentangling monitoring algorithm is able to accelerate the formation of the topographic map when compared with kMER. By capitalising on the efficacy of SOM-kMER in data classification and clustering, the applicability of SOM-kMER (and its variants) to decision support problems is demonstrated. The results obtained reveal that the proposed approach is able to integrate (i) human's knowledge, experience, and/or subjective judgements and (ii) the capability of the computer in processing data and information objectively into a unified framework for undertaking decision-making tasks.
Description
PhD
Keywords
Industrial technology , Hybrid artificial neural network