Data Analysis, Machine Learning and Knowledge Discovery

2014
ISBN: 978-3-319-01594-1
Verlag: Springer International Publishing

Buch, Englisch, 470 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 7372 g

Reihe: Studies in Classification, Data Analysis, and Knowledge Organization

Data analysis, machine learning and knowledge discovery are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and techniques that can be applied to a vast set of applications such as web and text mining, marketing, medicine, bioinformatics and business intelligence. This volume contains the revised versions of selected papers in the field of data analysis, machine learning and knowledge discovery presented during the 36th annual conference of the German Classification Society (GfKl). The conference was held at the University of Hildesheim (Germany) in August 2012.

Zielgruppe

Research

Autoren/Hrsg.

Spiliopoulou, Myra

Janning, Ruth

Schmidt-Thieme, Lars

Fachgebiete

• Mathematik | Informatik EDV | Informatik Angewandte Informatik Wirtschaftsinformatik
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsinformatik, SAP, IT-Management
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

AREA Statistics and Data Analysis: Classifcation, Cluster Analysis, Factor Analysis and Model Selection.- AREA Machine Learning and Knowledge Discovery: Clustering, Classifiers, Streams and Social Networks.- AREA Data Analysis and Classification in Marketing.- AREA Data Analysis in Finance.- AREA Data Analysis in Biostatistics and Bioinformatics.- AREA Interdisciplinary Domains: Data Analysis in Music, Education and Psychology.- LIS Workshop: Workshop on Classification and Subject Indexing in Library and Information Science.