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Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques (Massive Computing)

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This book will give the reader a perspective into the core theory and practice of data mining and knowledge discovery (DM and KD). Its chapters combine many theoretical foundations for various DM and KD methods, and they present a rich array of examples – many of which are drawn from real-life applications. Most of the theoretical developments discussed are accompanied by an extensive empirical analysis, which should give the reader both a deep theoretical and practical insight into the subjects covered.

The book presents the combined research experiences of its 40 authors gathered during a long search in gleaning new knowledge from data. The last page of each chapter has a brief biographical statement of its contributors, who are world-renowned experts.

As the information revolution replaced the industrial age an avalanche of massive data sets has spread all over the activities of engineering, science, medicine, finance, and other human endeavors. This book offers a nice pathway to the exploration of massive data sets.

The process of working with these massive data sets of information to extract useful knowledge (if such knowledge exists) is called knowledge discovery. Data mining is an important part of knowledge discovery in data sets. Knowledge discovery does not start and does not end with the data mining techniques. It also involves a clear understanding of the proposed applications, the creation of a target data set, removal or correction of corrupted data, data reduction, and needs an expert in the application field in order to decide if the patterns obtained by data mining are meaningful. The interpretation of the discovered patterns and the verification of their accuracy may also involve experts from different areas including visualization, image analysis and computer graphics.

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