Towards a scalable and efficient data classification technique

Data Classification is a task that could be found in many life activities. In general, the term could be used for any activity that derives some decision or forecast based on the currently available information. Using a more accurate definition, a classification procedure is the construction of some kind of a method for making judgments for a continuing sequence of cases, where each new case must be assigned to one of pre-defined classes. This type of construction has been termed supervised learning, in order to distinguish it from unsupervised learning or clustering in which the classes are not pre-defined but are concluded from the available data.; This thesis is divided into five chapters, analyzing three classification techniques, namely nearest neighbor technique, perceptron learning algorithm and multi-layer perceptrons with backpropagation, based on performance and scalability issues. Chapter one gives an introduction to the research topic of this thesis. (Abstract shortened by UMI.)...