Data Mining Applications with R

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

Produk Detail:

  • Author : Yanchang Zhao
  • Publisher : Academic Press
  • Pages : 514 pages
  • ISBN : 0124115209
  • Rating : 4.5/5 from 3 reviews
CLICK HERE TO GET THIS BOOKData Mining Applications with R

Data Mining Applications with R

Data Mining Applications with R
  • Author : Yanchang Zhao,Yonghua Cen
  • Publisher : Academic Press
  • Release : 26 November 2013
GET THIS BOOKData Mining Applications with R

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

R and Data Mining

R and Data Mining
  • Author : Yanchang Zhao
  • Publisher : Academic Press
  • Release : 31 December 2012
GET THIS BOOKR and Data Mining

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,

Data Mining with Rattle and R

Data Mining with Rattle and R
  • Author : Graham Williams
  • Publisher : Springer Science & Business Media
  • Release : 04 August 2011
GET THIS BOOKData Mining with Rattle and R

Data mining is the art and science of intelligent data analysis. By building knowledge from information, data mining adds considerable value to the ever increasing stores of electronic data that abound today. In performing data mining many decisions need to be made regarding the choice of methodology, the choice of data, the choice of tools, and the choice of algorithms. Throughout this book the reader is introduced to the basic concepts and some of the more popular algorithms of data

Data Mining with R

Data Mining with R
  • Author : Luis Torgo
  • Publisher : CRC Press
  • Release : 30 November 2016
GET THIS BOOKData Mining with R

Data Mining with R: Learning with Case Studies, Second Edition uses practical examples to illustrate the power of R and data mining. Providing an extensive update to the best-selling first edition, this new edition is divided into two parts. The first part will feature introductory material, including a new chapter that provides an introduction to data mining, to complement the already existing introduction to R. The second part includes case studies, and the new edition strongly revises the R code

Data Mining and Business Analytics with R

Data Mining and Business Analytics with R
  • Author : Johannes Ledolter
  • Publisher : John Wiley & Sons
  • Release : 28 May 2013
GET THIS BOOKData Mining and Business Analytics with R

Collecting, analyzing, and extracting valuable information froma large amount of data requires easily accessible, robust,computational and analytical tools. Data Mining and BusinessAnalytics with R utilizes the open source software R for theanalysis, exploration, and simplification of large high-dimensionaldata sets. As a result, readers are provided with the neededguidance to model and interpret complicated data and become adeptat building powerful models for prediction and classification. Highlighting both underlying concepts and practicalcomputational skills, Data Mining and Business Analytics withR begins with

Handbook of Statistical Analysis and Data Mining Applications

Handbook of Statistical Analysis and Data Mining Applications
  • Author : Robert Nisbet,Gary Miner,Ken Yale
  • Publisher : Elsevier
  • Release : 09 November 2017
GET THIS BOOKHandbook of Statistical Analysis and Data Mining Applications

Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and

Data Mining for Business Analytics

Data Mining for Business Analytics
  • Author : Galit Shmueli,Peter C. Bruce,Peter Gedeck,Nitin R. Patel
  • Publisher : John Wiley & Sons
  • Release : 14 October 2019
GET THIS BOOKData Mining for Business Analytics

Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities. This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction,

Text Mining with R

Text Mining with R
  • Author : Julia Silge,David Robinson
  • Publisher : "O'Reilly Media, Inc."
  • Release : 12 June 2017
GET THIS BOOKText Mining with R

Much of the data available today is unstructured and text-heavy, making it challenging for analysts to apply their usual data wrangling and visualization tools. With this practical book, you’ll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr. You’ll learn how tidytext and other tidy tools in R can make text analysis easier and more effective. The authors demonstrate how treating

Practical Graph Mining with R

Practical Graph Mining with R
  • Author : Nagiza F. Samatova,William Hendrix,John Jenkins,Kanchana Padmanabhan,Arpan Chakraborty
  • Publisher : CRC Press
  • Release : 15 July 2013
GET THIS BOOKPractical Graph Mining with R

Discover Novel and Insightful Knowledge from Data Represented as a GraphPractical Graph Mining with R presents a "do-it-yourself" approach to extracting interesting patterns from graph data. It covers many basic and advanced techniques for the identification of anomalous or frequently recurring patterns in a graph, the discovery of groups or cluste

Data Mining for Business Analytics

Data Mining for Business Analytics
  • Author : Galit Shmueli,Peter C. Bruce,Nitin R. Patel
  • Publisher : John Wiley & Sons
  • Release : 18 April 2016
GET THIS BOOKData Mining for Business Analytics

Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition presents an applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies. Readers will work with all of the standard data mining methods using the Microsoft® Office Excel® add-in XLMiner® to develop predictive models and learn how to obtain business value from Big Data. Featuring updated topical coverage on text mining, social network analysis, collaborative filtering, ensemble methods, uplift modeling

Data Analysis and Applications 1

Data Analysis and Applications 1
  • Author : Christos H. Skiadas,James R. Bozeman
  • Publisher : Wiley-ISTE
  • Release : 02 April 2019
GET THIS BOOKData Analysis and Applications 1

This series of books collects a diverse array of work that provides the reader with theoretical and applied information on data analysis methods, models, and techniques, along with appropriate applications. Volume 1 begins with an introductory chapter by Gilbert Saporta, a leading expert in the field, who summarizes the developments in data analysis over the last 50 years. The book is then divided into three parts: Part 1 presents clustering and regression cases; Part 2 examines grouping and decomposition, GARCH and threshold models, structural

Data Mining With Decision Trees: Theory And Applications (2nd Edition)

Data Mining With Decision Trees: Theory And Applications (2nd Edition)
  • Author : Maimon Oded Z,Rokach Lior
  • Publisher : World Scientific
  • Release : 03 September 2014
GET THIS BOOKData Mining With Decision Trees: Theory And Applications (2nd Edition)

Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining; it is the science of exploring large and complex bodies of data in order to discover useful patterns. Decision tree learning continues to evolve over time. Existing methods are constantly being improved and new methods introduced.This 2nd Edition is dedicated entirely to the field of decision trees in data mining; to cover all aspects of this important technique, as well as

R Data Mining

R Data Mining
  • Author : Andrea Cirillo
  • Publisher : Packt Publishing Ltd
  • Release : 29 November 2017
GET THIS BOOKR Data Mining

Mine valuable insights from your data using popular tools and techniques in R About This Book Understand the basics of data mining and why R is a perfect tool for it. Manipulate your data using popular R packages such as ggplot2, dplyr, and so on to gather valuable business insights from it. Apply effective data mining models to perform regression and classification tasks. Who This Book Is For If you are a budding data scientist, or a data analyst with

Applied Data Mining

Applied Data Mining
  • Author : Guandong Xu,Yu Zong,Zhenglu Yang
  • Publisher : CRC Press
  • Release : 17 June 2013
GET THIS BOOKApplied Data Mining

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.

Data Mining and Knowledge Discovery for Geoscientists

Data Mining and Knowledge Discovery for Geoscientists
  • Author : Guangren Shi
  • Publisher : Elsevier
  • Release : 09 October 2013
GET THIS BOOKData Mining and Knowledge Discovery for Geoscientists

Currently there are major challenges in data mining applications in the geosciences. This is due primarily to the fact that there is a wealth of available mining data amid an absence of the knowledge and expertise necessary to analyze and accurately interpret the same data. Most geoscientists have no practical knowledge or experience using data mining techniques. For the few that do, they typically lack expertise in using data mining software and in selecting the most appropriate algorithms for a