Data Mining Methods for Knowledge Discovery provides an introduction to the data mining methods that are frequently used in the process of knowledge discovery. This book first elaborates on the fundamentals of each of the data mining methods: rough sets, Bayesian analysis, fuzzy sets, genetic algorithms, machine learning, neural networks, and preprocessing techniques. The book then goes on to thoroughly discuss these methods in the setting of the overall process of knowledge discovery. Numerous illustrative examples and experimental findings are also included. Each chapter comes with an extensive bibliography. Data Mining Methods for Knowledge Discovery is intended for senior undergraduate and graduate students, as well as a broad audience of professionals in computer and information sciences, medical informatics, and business information systems.

Modern medicine generates, almost daily, huge amounts of heterogeneous data. For example, medical data may contain SPECT images, signals lik e ECG, clinical information like temperature, cholesterol levels, etc., as well as the physician's interpretation. Those who deal with such data understand that there is a widening gap between data collection a nd data comprehension. Computerized techniques are needed to help huma ns address this problem. This volume is devoted to the relatively youn g and growing field of medical data mining and knowledge discovery. As more and more medical procedures employ imaging as a preferred diagno stic tool, there is a need to develop methods for efficient mining in databases of images. Other significant features are security and confi dentiality concerns. Moreover, the physician's interpretation of image s, signals, or other technical data, is written in unstructured Englis h which is very difficult to mine. This book addresses all these speci fic features.