Simon M. Garrett

Learn More
The field of Artificial Immune Systems (AIS) concerns the study and development of computationally interesting abstractions of the immune system. This survey tracks the development of AIS since its inception, and then attempts to make an assessment of its usefulness, defined in terms of 'distinctiveness' and 'effectiveness.' In this paper, the standard(More)
Cluster ensembles have recently emerged as a powerful alternative to standard cluster analysis, aggregating several input data clusterings to generate a single output clustering, with improved robustness and stability. From the early work, these techniques held great promise; however, most of them generate the final solution based on incomplete information(More)
MOTIVATION It is far from trivial to select the most effective clustering method and its parameterization, for a particular set of gene expression data, because there are a very large number of possibilities. Although many researchers still prefer to use hierarchical clustering in one form or another, this is often sub-optimal. Cluster ensemble research(More)
In this paper, we examine the clonal selection algorithm CLONALG and the suggestion that it is suitable for pattern recognition. CLONALG is tested over a series of binary character recognition tasks and its performance compared to a set of basic binary matching algorithms. A number of enhancements are made to the algorithm to improve its performance and the(More)
The vertebrate immune system is a robust and powerful information process system that demonstrates features such as distributed control, parallel processing and adaptation and learning via experience. Artificial Immune Systems (AIS) are machine-learning algorithms that embody some of the principles and attempt to take advantages of the benefits of natural(More)
Although attempts have been made to solve the problem of clustering categorical data via cluster ensembles, with the results being competitive to conventional algorithms, it is observed that these techniques unfortunately generate a final data partition based on incomplete information. The underlying ensemble-information matrix presents only cluster-data(More)
Motivation Perhaps the greatest challenge of modern biology is to develop accurate in silico models of cells. To do this we require computational formalisms for both simulation (how according to the model the state of the cell evolves over time), and identification (learning a model cell from observation of states). We propose the use of qualitative(More)