Kenneth McGarry

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It is a well known fact that the data mining process can generate many hundreds and often thousands of patterns from data. The task for the data miner then becomes one of determining the most useful patterns from those that are trivial or are already well known to the organization. It is therefore necessary to filter out those patterns through the use of(More)
Recently there has been a lot of interest in the extrac tion of symbolic rules from neural networks The work described in this paper is concerned with an evaluation and comparison of the accuracy and complexity of sym bolic rules extracted from radial basis function networks and multi layer perceptrons Here we examine the abil ity of rule extraction(More)
The Kohonen self-organising feature map (SOM) has several important properties that can be used within the data mining/knowledge discovery and exploratory data analysis process. A key characteristic of the SOM is its topology preserving ability to map a multi-dimensional input into a two-dimensional form. This feature is used for classification and(More)
Many users of image retrieval systems would prefer to express initial queries using keywords. However, manual keyword indexing is very time-consuming. Therefore, a content-based image retrieval system which can automatically assign keywords to images would be very attractive. Unfortunately, it has proved very challenging to build such systems, except where(More)
Radial basis neural (RBF) networks provide an excellent solution to many pattern recognition and classi cation problems. However, RBF networks are also a local representation technique that enables the easy conversion of the hidden units into symbolic rules. This paper examines rules extracted from RBF networks. We use the iris ower classication task and a(More)
Proteomics is a field dedicated to the analysis and identification of proteins within an organism. Within proteomics, two-dimensional electrophoresis (2-DE) is currently unrivalled as a technique to separate and analyse proteins from tissue samples. The analysis of postexperimental data produced from this technique has been identified as an important step(More)
This paper describes techniques for integrating neural networks and symbolic components into powerful hybrid systems. Neural networks have unique processing characteristics that enable tasks to be performed that would be di cult or intractable for a symbolic rule-based system. However, a stand-alone neural network requires an interpretation either by a(More)
The self-organizing map (SOM) is useful within bioinformatics research because of its clustering and visualization capabilities. The SOM is a vector quantization method that reduces the dimensionality of original measurement and visualizes individual tumor sample in a SOM component plane. The data is taken from cDNA microarray experiments on Diffuse Large(More)