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With the growing use of information technology and the recent advances in web systems, the amount of data available to users has increased exponentially. Thus, there is a critical need to understand the content of the data. As a result, data-mining has become a popular research topic in recent years for the treatment of the "data rich and information poor" syndrome. In this carefully edited volume a theoretical foundation as well as important new directions for data-mining research are presented. It brings together a set of well respected data mining theoreticians and researchers with practical data mining experiences. The presented theories will give data mining practitioners a scientific perspective in data mining and thus provide more insight into their problems, and the provided new data mining topics can be expected to stimulate further research in these important directions.

Engineering sciences. Technology --- Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- ICT (informatie- en communicatietechnieken) --- analyse (wiskunde) --- data mining --- informatiesystemen --- KI (kunstmatige intelligentie) --- ingenieurswetenschappen --- robots --- AI (artificiële intelligentie)

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This book contains valuable studies in data mining from both foundational and practical perspectives. The foundational studies of data mining may help to lay a solid foundation for data mining as a scientific discipline, while the practical studies of data mining may lead to new data mining paradigms and algorithms. The foundational studies contained in this book focus on a broad range of subjects, including conceptual framework of data mining, data preprocessing and data mining as generalization, probability theory perspective on fuzzy systems, rough set methodology on missing values, inexact multiple-grained causal complexes, complexity of the privacy problem, logical framework for template creation and information extraction, classes of association rules, pseudo statistical independence in a contingency table, and role of sample size and determinants in granularity of contingency matrix. The practical studies contained in this book cover different fields of data mining, including rule mining, classification, clustering, text mining, Web mining, data stream mining, time series analysis, privacy preservation mining, fuzzy data mining, ensemble approaches, and kernel based approaches. We believe that the works presented in this book will encourage the study of data mining as a scientific field and spark collaboration among researchers and practitioners.

Engineering sciences. Technology --- Artificial intelligence. Robotics. Simulation. Graphics --- analyse (wiskunde) --- data mining --- KI (kunstmatige intelligentie) --- ingenieurswetenschappen --- robots