Wing-Ho Shum

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The Self-Organizing Map (SOM) is a powerful tool in the exploratory phase of data mining. It is capable of projecting high-dimensional data onto a regular, usually 2-dimensional grid of neurons with good neighborhood preservation between two spaces. However, due to the dimensional conflict, the neighborhood preservation cannot always lead to perfect(More)
The Self-Organizing Map (SOM) is a powerful tool in the exploratory phase of data mining. However, due to the dimensional conflict, the neighborhood preservation cannot always lead to perfect topology preservation. In this paper , we establish an Expanding SOM (ESOM) to detect and preserve better topology correspondence between the two spaces. Our(More)
A novel Genetic Programming (GP) paradigm called Co-evolutionary Rule-Chaining Genetic Programming (CRGP) has been proposed to learn the relationships among attributes represented by a set of classification rules for multi-class problems. It employs backward chaining inference to carry out classification based on the acquired acyclic rule set. Its main(More)
Bayesian Network (BN) is a powerful network model, which represents a set of variables in the domain and provides the probabilistic relationships among them. But BN can handle discrete values only; it cannot handle continuous , interval and ordinal ones, which must be converted to discrete values and the order information is lost. Thus, BN tends to have(More)
— Classification rule is a useful model in data mining. Given variable values, rules classify data items into different classes. Different rule learning algorithms are proposed, like Genetic Algorithm (GA) and Genetic Programming (GP). Rules can also be extracted from Bayesian Network (BN) and decision trees. However, all of them have disadvantages and may(More)
Multi-class problem is the class of problems having more than one classes in the data set. Bayesian Network (BN) is a well-known algorithm handling the multi-class problem and is applied to different areas. But BN cannot handle continuous values. In contrast, Genetic Programming (GP) can handle continuous values and produces classification rules. However,(More)
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