R and Data Mining: Examples and Case Studies [NOOK Book]

Overview

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 ...

See more details below
R and Data Mining: Examples and Case Studies

Available on NOOK devices and apps  
  • NOOK Devices
  • Samsung Galaxy Tab 4 NOOK 7.0
  • Samsung Galaxy Tab 4 NOOK 10.1
  • NOOK HD Tablet
  • NOOK HD+ Tablet
  • NOOK eReaders
  • NOOK Color
  • NOOK Tablet
  • Tablet/Phone
  • NOOK for Windows 8 Tablet
  • NOOK for iOS
  • NOOK for Android
  • NOOK Kids for iPad
  • PC/Mac
  • NOOK for Windows 8
  • NOOK for PC
  • NOOK for Mac
  • NOOK for Web

Want a NOOK? Explore Now

NOOK Book (eBook)
$45.99
BN.com price
(Save 42%)$79.95 List Price

Overview

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
Read More Show Less

Product Details

  • ISBN-13: 9780123972712
  • Publisher: Elsevier Science
  • Publication date: 12/31/2012
  • Sold by: Barnes & Noble
  • Format: eBook
  • Pages: 256
  • File size: 7 MB

Meet the Author

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.
Read More Show Less

Table of Contents

  1. Introduction
    1. Introduction, Data mining
      1. R
      2. Datasets used in this book
  2. Data Loading and Exploration
    1. Data Import/Export
      1. Save/Load R Data
      2. Import from and Export to .CSV Files
      3. Import Data from SAS
      4. Import/Export via ODBC
    2. Data Exploration
      1. Have a Look at Data
      2. Explore Individual Variables
      3. Explore Multiple Variables
      4. More Exploration
      5. Save Charts as Files
  3. Data Mining Examples
    1. Decision Trees
      1. Building Decision Trees with Package party
      2. Building Decision Trees with Package rpart
      3. Random Forest
    2. Regression
      1. Linear Regression
      2. Logistic Regression
      3. Generalized Linear Regression
      4. Non-linear Regression
    3. Clustering
      1. K-means Clustering
      2. Hierarchical Clustering
      3. Density-based Clustering
    4. Outlier Detection
    5. Time Series Analysis
      1. Time Series Decomposition
      2. Time Series Forecast
    6. Association Rules
    7. Sequential Patterns
    8. Text Mining
    9. Social Network Analysis
  4. Case Studies
    1. Case Study I: Analysis and Forecasting of House Price Indices
      1. Reading Data from a CSV File
      2. Data Exploration
      3. Time Series Decomposition
      4. Time Series Forecasting
      5. Discussion
    2. Case Study II: Customer Response Prediction
    3. Case Study III: Risk Rating using Decision Tree with Limited Resources
    4. Customer Behaviour Prediction and Intervention
  5. Appendix
    1. Online Resources
    2. R Reference Card for Data Mining

Bibliography

Read More Show Less

Customer Reviews

Be the first to write a review
( 0 )
Rating Distribution

5 Star

(0)

4 Star

(0)

3 Star

(0)

2 Star

(0)

1 Star

(0)

Your Rating:

Your Name: Create a Pen Name or

Barnes & Noble.com Review Rules

Our reader reviews allow you to share your comments on titles you liked, or didn't, with others. By submitting an online review, you are representing to Barnes & Noble.com that all information contained in your review is original and accurate in all respects, and that the submission of such content by you and the posting of such content by Barnes & Noble.com does not and will not violate the rights of any third party. Please follow the rules below to help ensure that your review can be posted.

Reviews by Our Customers Under the Age of 13

We highly value and respect everyone's opinion concerning the titles we offer. However, we cannot allow persons under the age of 13 to have accounts at BN.com or to post customer reviews. Please see our Terms of Use for more details.

What to exclude from your review:

Please do not write about reviews, commentary, or information posted on the product page. If you see any errors in the information on the product page, please send us an email.

Reviews should not contain any of the following:

  • - HTML tags, profanity, obscenities, vulgarities, or comments that defame anyone
  • - Time-sensitive information such as tour dates, signings, lectures, etc.
  • - Single-word reviews. Other people will read your review to discover why you liked or didn't like the title. Be descriptive.
  • - Comments focusing on the author or that may ruin the ending for others
  • - Phone numbers, addresses, URLs
  • - Pricing and availability information or alternative ordering information
  • - Advertisements or commercial solicitation

Reminder:

  • - By submitting a review, you grant to Barnes & Noble.com and its sublicensees the royalty-free, perpetual, irrevocable right and license to use the review in accordance with the Barnes & Noble.com Terms of Use.
  • - Barnes & Noble.com reserves the right not to post any review -- particularly those that do not follow the terms and conditions of these Rules. Barnes & Noble.com also reserves the right to remove any review at any time without notice.
  • - See Terms of Use for other conditions and disclaimers.
Search for Products You'd Like to Recommend

Recommend other products that relate to your review. Just search for them below and share!

Create a Pen Name

Your Pen Name is your unique identity on BN.com. It will appear on the reviews you write and other website activities. Your Pen Name cannot be edited, changed or deleted once submitted.

 
Your Pen Name can be any combination of alphanumeric characters (plus - and _), and must be at least two characters long.

Continue Anonymously

    If you find inappropriate content, please report it to Barnes & Noble
    Why is this product inappropriate?
    Comments (optional)