Scalable Rule Induction Systems

Rule induction as a method for constructing classifiers is particularly attractive in data mining applications, where the comprehensibility of the generated models is very important. Most existing techniques were designed for small data sets and thus are not practical for direct use on very large data sets because of their computational inefficiency. Scaling up rule induction methods to handle such data sets is a formidable challenge. This book presents new scalable algorithms for rule induction that can process large data sets efficiently while building from them the best possible models. These algorithms were tested on several complex real-world data sets and the results proved that they scaled up well and were extremely effective learners. The book should be especially useful to information systems practitioners, programmers, consultants, developers, information technology managers, as well as students and professors who are interested in the principles and ideas underlying the current practice of data mining.