Data Analysis, Machine Learning and Knowledge Discovery
1st Edition
Published on 26. November 2013
XXI, 470 pages
978-3-319-01595-8 (ISBN)
Description
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.
Reviews / Votes
From the book reviews:
"The book is organized in seven parts . . The book is a very interesting collection of papers describing various approaches of data mining and machine learning on aspects from bioinformatics to music classification. It is an excellent addition to the field and it can be used as starting point for projects from undergraduate to post-graduate level." (Irina Ioana Mohorianu, zbMATH, Vol. 1301, 2015)More details
Series
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Product notice
Reflowable
Illustrations
XXI, 470 p. 120 illus., 32 illus. in color.
File size
8,87 MB
ISBN-13
978-3-319-01595-8 (9783319015958)
DOI
10.1007/978-3-319-01595-8
Schweitzer Classification
Other editions
Additional editions
Myra Spiliopoulou | Lars Schmidt-Thieme | Ruth Janning
Data Analysis, Machine Learning and Knowledge Discovery
Book
12/2013
Springer
€106.99
Shipment within 10-15 days
Content
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.
