R and Data Mining : Examples and Case Studies

Yanchang Zhao

ISBN 10: 0123969638 / ISBN 13: 9780123969637

Editore: Elsevier Science & Technology, Academic Press, 2012

Riguardo questo articolo

nach der Bestellung gedruckt Neuware - Printed after ordering - R and Data Mining introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more.Data mining techniques are growing in popularity in a broad range of areas, from banking to insurance, retail, telecom, medicine, research, and government. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation.With three in-depth case studies, a quick reference guide, bibliography, and links to a wealth of online resources, R and Data Mining is a valuable, practical guide to a powerful method of analysis.

Codice articolo 9780123969637

Dati bibliografici

• Titolo: R and Data Mining : Examples and Case Studies
• Editore: Elsevier Science & Technology, Academic Press
• Data di pubblicazione: 2012
• Lingua: Inglese
• ISBN 10: 0123969638
• ISBN 13: 9780123969637
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 228 L x 152 A x 15 P (millimetri) 228 millimetri di larghezza per 152 millimetri di altezza per 15 millimetri di profondità
• Peso: 507 grammi

Descrizione articolo

Riassunto

This book introduces using R for data mining. Data mining techniques are widely used in government agencies, banks, insurance, retail, telecom, medicine and research. Recently, there is an increasing tendency to do data mining with R, a free software environment for statistical computing and graphics. According to a poll by KDnuggets.com in early 2011, R is the 2^nd popular tool for data mining work. By introducing using R for data mining, this book will have a broad audience from both academia and industry. It targets researchers in the field of data mining, postgraduate students who are interested in data mining, and data miners and analysts from industry. For example, many universities have courses on data mining, and the proposed book will be a useful reference for students learning data mining in those courses. There are also many training courses on data mining in industry, such as training by SAS and IBM on data mining. The book will be of interest to the course learners as well.

• The book will present an introduction into using R for data mining applications, covering most popular data mining techniques.
• Code examples and data will be provided, so that readers can easily learn the techniques.
• Case studies in real-world applications will be covered, which will help readers to apply the techniques in their work.

Informazioni sull?autore

A Senior Data Mining Analyst in Australia Government since 2009.

Before joining public sector, he was an Australian Postdoctoral Fellow (Industry) in the Faculty of Engineering & Information Technology at University of Technology, Sydney, Australia. His research interests include clustering, association rules, time series, outlier detection and data mining applications and he has over forty papers published in journals and conference proceedings. He is a member of the IEEE and a member of the Institute of Analytics Professionals of Australia, and served as program committee member for more than thirty international conferences.