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Data mining: foundations and practice

Contributor(s): Lin, Tsau Young [Editor] | Xie, Ying [Editor] | Wasilewska, Anita [Editor] | Liau, Churn-Jung [Editor].
Material type: materialTypeLabelBookSeries: Studies in computational intelligence, vol. 118. Publisher: Berlin Springer 2008Description: xv, 25 p.ISBN: 9783540784876.Subject(s): Data miningDDC classification: 006.312 Summary: 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.
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Slot 104 (0 Floor, West Wing) Non-fiction 006.312 D2 (Browse shelf) Available 178966

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.

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