The content of this book includes valuable studies of data mining from both foundational and practical perspectives. The foundational studies of data mining may help laying sound foundations for data mining as a scientific discipline. The foundational studies contained in this book include: 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 of data mining may lead to new data mining paradigms and novel data mining algorithms. Presents foundations and practice of Data Mining INDICE: From the contents Compact Representations of Sequential Classification Rules.- An Algorithm for Mining Weighted Dense Maximal 1-complete Regions.- Mining Linguistic Trends from Time Series.- Latent Semantic Space for Web Clustering.- A Logical Framework for Template Creation and Information Extraction.- A Probability Theory Perspective on The Zadeh Fuzzy System.- Three Approaches to Missing Attribute Values—A Rough Set Perspective.- MLEM2 Rule Induction Algorithms: with and without Merging Intervals.- Towards a Methodology for Data mining Project Development: The Importance of Abstraction.- Fining Active Membership Functions in Fuzzy Data Mining.- A Compressed Vertical Binary Algorithm for Mining Frequent Patterns.- Inexact Multiple-Grained Causal Complexes.
- ISBN: 978-3-540-78487-6
- Editorial: Springer
- Encuadernacion: Cartoné
- Páginas: 530
- Fecha Publicación: 01/06/2008
- Nº Volúmenes: 1
- Idioma: Inglés