Data Mining A Knowledge Discovery Approach /

• Κύριοι συγγραφείς: Cios, Krzysztof J. (Συγγραφέας), Swiniarski, Roman W. (Συγγραφέας), Pedrycz, Witold (Συγγραφέας), Kurgan, Lukasz A. (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Boston, MA : Springer US, 2007.
• Θέματα: Computer science.; Database management.; Data mining.; Information storage and retrieval.; Artificial intelligence.; Pattern recognition.; Statistics.; Computer Science.; Database Management.; Artificial Intelligence (incl. Robotics).; Data Mining and Knowledge Discovery.; Information Storage and Retrieval.; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.; Pattern Recognition.

 This comprehensive textbook on data mining details the unique steps of the knowledge discovery process that prescribe the sequence in which data mining projects should be performed. Data Mining offers an authoritative treatment of all development phases from problem and data understanding through data preprocessing to deployment of the results. This knowledge discovery approach is what distinguishes this book from other texts in the area. It concentrates on data preparation, clustering and association rule learning (required for processing unsupervised data), decision trees, rule induction algorithms, neural networks, and many other data mining methods, focusing predominantly on those which have proven successful in data mining projects. Based upon the authors’ previous successful book on data mining and knowledge discovery, this new volume has been extensively expanded, making it an effective instructional tool for advanced-level undergraduate and graduate courses. This book offers: A suite of exercises at the end of every chapter, designed to enhance the reader’s understanding of the theory and proficiency with the tools presented Links to all-inclusive instructional presentations for each chapter to ensure easy use in classroom teaching Extensive appendices covering relevant mathematical material for convenient look-up Methods for addressing issues related to data privacy and security within the context of data mining, enabling the reader to balance potentially conflicting aims Summaries and bibliographical notes for each chapter, providing a broader perspective of the concepts and methods described Researchers, practitioners and students are certain to consider this volume an indispensable resource in successfully accomplishing the goals of their data mining projects. . 

 Data Mining and Knowledge Discovery Process -- The Knowledge Discovery Process -- Data Understanding -- Data -- Concepts of Learning, Classification, and Regression -- Knowledge Representation -- Data Preprocessing -- Databases, Data Warehouses, and OLAP -- Feature Extraction and Selection Methods -- Discretization Methods -- Data Mining: Methods for Constructing Data Models -- Unsupervised Learning: Clustering -- Unsupervised Learning: Association Rules -- Supervised Learning: Statistical Methods -- Supervised Learning: Decision Trees, Rule Algorithms, and Their Hybrids -- Supervised Learning: Neural Networks -- Text Mining -- Data Models Assessment -- Assessment of Data Models -- Data Security and Privacy Issues -- Data Security, Privacy and Data Mining.