
Applied Data Mining
CRC Press
1st Edition
Published on 17. June 2013
Book
Hardback
284 pages
978-1-4665-8583-6 (ISBN)
Description
Data mining has witnessed substantial advances in recent decades. New research questions and practical challenges have arisen from emerging areas and applications within the various fields closely related to human daily life, e.g. social media and social networking. This book aims to bridge the gap between traditional data mining and the latest advances in newly emerging information services. It explores the extension of well-studied algorithms and approaches into these new research arenas.
More details
Language
English
Place of publication
Bosa Roca
United States
Publishing group
Taylor & Francis Inc
Target group
College/higher education
Illustrations
2 farbige Abbildungen, 58 s/w Abbildungen
2 Illustrations, color; 58 Illustrations, black and white
Dimensions
Height: 240 mm
Width: 161 mm
Thickness: 20 mm
Weight
596 gr
ISBN-13
978-1-4665-8583-6 (9781466585836)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Guandong Xu | Yu Zong | Zhenglu Yang
Applied Data Mining
E-Book
06/2013
1st Edition
CRC Press
€140.09
Available for download
Persons
Dr. Guandong Xu , Centre for Applied Informatics, School of Engineering and Science, Victoria University, Australia
Prof. Yu Zong, Department of Information and Engineering, West Anhui University, China
Dr. Zhenglu Yang, Institute of Industrial Science, Tokyo, Japan
Prof. Yu Zong, Department of Information and Engineering, West Anhui University, China
Dr. Zhenglu Yang, Institute of Industrial Science, Tokyo, Japan
Content
FUNDAMENTALS. Introduction. Mathematical Foundations. Data Preparation. Cluster Analysis. Classification. Frequent Pattern Mining. ADVANCED DATA MINING. Advanced Clustering Analysis. Privacy Preserving in Data Mining. Data Stream. EMERGING APPLICATIONS. Web Clustering and Web Community. Recommender Systems. Data Mining in Social Tagging Systems. Social Network Mining.