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Data mining is beneficial for anyone who has a huge amount of data, for example, customer and business data, transaction, marketing, financial, manufacturing and web data etc. The results of data mining are also referred to as knowledge in the form of rules, regularities and constraints. Rule mining is one of the popular data mining methods since rules provide concise statements of potentially important information that is easily understood by end users and also actionable patterns. At present rule mining has received a good deal of attention and enthusiasm from data mining researchers since rule mining is capable of solving many data mining problems such as classification, association, customer profiling, summarization, segmentation and many others. This book makes several contributions by proposing rule mining methods using genetic algorithms and neural networks. These methods are based on an integrated framework but capable of mining three major classes of rules e.g. Association, Characteristic and Classification rules. The book also shows how to build data mining predictive models using the resultant rules of the proposed methods.

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Sardar Rahman received PhD in Computer and Electrical Engineering from Royal Melbourne Institute of Technology, Australia, in 2006. Before PhD, he was in charge of the Data Mining and Predictive Modelling R&D group at CorVu. He is currently an Associate Professor at Computer Science and Engineering Department, United International University.

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