Buch, Englisch, 369 Seiten, Format (B × H): 161 mm x 242 mm, Gewicht: 1590 g
Buch, Englisch, 369 Seiten, Format (B × H): 161 mm x 242 mm, Gewicht: 1590 g
Reihe: Advanced Information and Knowledge Processing
ISBN: 978-1-85233-989-0
Verlag: Springer
Knowledge discovery takes the raw results from data mining (the process of extracting trends or patterns from data) and carefully and accurately transforms them into useful and understandable information. In this book, active practitioners and leading researchers detail recent advances in knowledge discovery. Coverage presents a good balance of introductory material on the knowledge discovery process, advanced issues, and state-of-the-art tools and techniques. An overview of the field, looking at the issues and challenges involved, is followed by coverage of recent trends and important applications of advanced data mining techniques in areas such as life sciences, world-wide web, image databases, cyber security, and sensor networks.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Zeichen- und Zahlendarstellungen
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Grafikprogrammierung
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
- Mathematik | Informatik EDV | Informatik Informatik Logik, formale Sprachen, Automaten
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenkompression, Dokumentaustauschformate
Weitere Infos & Material
Foundations.- Knowledge Discovery and Data Mining.- Automatic Discovery of Class Hierarchies via Output Space Decomposition.- Graph-based Mining of Complex Data.- Predictive Graph Mining with Kernel Methods.- TreeMiner: An Efficient Algorithm for Mining Embedded Ordered Frequent Trees.- Sequence Data Mining.- Link-based Classification.- Applications.- Knowledge Discovery from Evolutionary Trees.- Ontology-Assisted Mining of RDF Documents.- Image Retrieval using Visual Features and Relevance Feedback.- Significant Feature Selection Using Computational Intelligent Techniques for Intrusion Detection.- On-board Mining of Data Streams in Sensor Networks.- Discovering an Evolutionary Classifier over a High-speed Nonstatic Stream.
