Yanchang Zhao

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. Presents an introduction into using R for data mining applications, covering most popular data mining techniques Provides code examples and data so that readers can easily learn the techniques Features case studies in real-world applications to help readers apply the techniques in their work

Examples and Case Studies

R and Data Mining

Graham Williams

Thuc D. Le

Kok-Leong Ong

Warren H. Jin

Sebastien Wong

Lin Liu

This book constitutes the refereed proceedings of the 17th Australasian Conference on Data Mining, AusDM 2019, held in Adelaide, SA, Australia, in December 2019. The 20 revised full papers presented were carefully reviewed and selected from 56 submissions. The papers are organized in sections on research track, application track, and industry showcase.

17th Australasian Conference, AusDM 2019, Adelaide, SA, Australia, December 2–5, 2019, Proceedings

Data Mining

Chang-Tsun Li

Rafiqul Islam

David Stirling

Yun Sing Koh

Graco Warwick

Zahidul Islam

This book constitutes the refereed proceedings of the 16th Australasian Conference on Data Mining, AusDM 2018, held in Bathurst, NSW, Australia, in November 2018. The 27 revised full papers presented together with 3 short papers were carefully reviewed and selected from 80 submissions. The papers are organized in topical sections on classification task; transport, environment, and energy; applied data mining; privacy and clustering; statistics in data science; health, software and smartphone; image data mining; industry showcase.

16th Australasian Conference, AusDM 2018, Bahrurst, NSW, Australia, November 28–30, 2018, Revised Selected Papers

Yonghua Cen

Data Mining Applications with R is a great resource for researchers and professionals to understand the wide use of R, a free software environment for statistical computing and graphics, in solving different problems in industry. R is widely used in leveraging data mining techniques across many different industries, including government, finance, insurance, medicine, scientific research and more. This book presents 15 different real-world case studies illustrating various techniques in rapidly growing areas. It is an ideal companion for data mining researchers in academia and industry looking for ways to turn this versatile software into a powerful analytic tool. R code, Data and color figures for the book are provided at the RDataMining.com website. Helps data miners to learn to use R in their specific area of work and see how R can apply in different industries Presents various case studies in real-world applications, which will help readers to apply the techniques in their work Provides code examples and sample data for readers to easily learn the techniques by running the code by themselves

Data Mining Applications with R

Longbing Cao

Chengqi Zhang

Philip S. Yu

This book offers state-of the-art research and development outcomes on methodologies, techniques, approaches and successful applications in domain driven, actionable knowledge discovery. It bridges the gap between business expectations and research output.

Domain Driven Data Mining

Yanchang Zhao

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