Buch, Englisch, 606 Seiten, Format (B × H): 183 mm x 260 mm, Gewicht: 1354 g
A Knowledge Discovery Approach
Buch, Englisch, 606 Seiten, Format (B × H): 183 mm x 260 mm, Gewicht: 1354 g
ISBN: 978-0-387-33333-5
Verlag: Springer US
This comprehensive textbook on data mining details the unique steps of the knowledge discovery process that prescribes the sequence in which projects should be performed, from data understanding and preprocessing to deployment of the results. This knowledge discovery approach is what distinguishes Data Mining from other texts in this area. The book provides a suite of exercises and includes links to instructional presentations. Furthermore, it contains appendices covering relevant mathematical material.
The authors' experience and expertise ensure that this text will be an authoritative instructional tool. Researchers, practitioners and students are certain to consider this text an indispensable resource in successfully accomplishing the goals of their data mining projects.
Zielgruppe
Advanced-level students, researchers and practitioners in computer science
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Zeichen- und Zahlendarstellungen
- Mathematik | Informatik EDV | Informatik Technische Informatik Systemverwaltung & Management
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenkompression, Dokumentaustauschformate
- Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse
Weitere Infos & Material
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.
