Data analysis using artificial immune systems, cluster analysis and Kohonen networks: some comparisons

Abstract

Knowledge discovery in databases (KDD) is still a relatively new and expanding field. To aid the KDD process, data mining methods are used to extract previously unknown patterns and trends in vast amounts of data. There exist a number of data mining techniques, taking methods from the machine learning, statistical analysis and pattern recognition communities, to name a few. Each technique has something different to offer over other techniques and each is suitable for different purposes giving certain benefits in varying situations. This paper examines a novel data analysis technique that is inspired by the human immune system: the artificial immune system (AIS). Immune system principles act as inspiration, allowing the creation of a network of cells that in effect clusters similar patterns and trends together. It is inspired by but not a model of the human immune system. This clustering allows the human user to effectively identify areas of similarity from the training data set that would previously have been unobtainable.