R 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, 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

Produk Detail:

  • Author : Yanchang Zhao
  • Publisher : Academic Press
  • Pages : 256 pages
  • ISBN : 012397271X
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKR and Data Mining

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 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 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 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

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

Learning Data Mining with R

Learning Data Mining with R
  • Author : Bater Makhabel
  • Publisher : Packt Publishing Ltd
  • Release : 31 January 2015
GET THIS BOOKLearning Data Mining with R

This book is intended for the budding data scientist or quantitative analyst with only a basic exposure to R and statistics. This book assumes familiarity with only the very basics of R, such as the main data types, simple functions, and how to move data around. No prior experience with data mining packages is necessary; however, you should have a basic understanding of data mining concepts and processes.

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

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

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,

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

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

The Elements of Statistical Learning

The Elements of Statistical Learning
  • Author : Trevor Hastie,Robert Tibshirani,Jerome Friedman
  • Publisher : Springer Science & Business Media
  • Release : 11 November 2013
GET THIS BOOKThe Elements of Statistical Learning

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This