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R and Data Mining: Examples and Case Studies 1st Edition

2.2 out of 5 stars 5 customer reviews
ISBN-13: 978-0123969637
ISBN-10: 0123969638
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Editorial Reviews

From the Author

Table of Contents:

1 Introduction   
    1.1 Data Mining
    1.2 R
    1.3 Datasets
        1.3.1 The Iris Dataset
        1.3.2 The Bodyfat Dataset

2 Data Import and Export
    2.1 Save and Load R Data
    2.2 Import from and Export to .CSV Files
    2.3 Import Data from SAS
    2.4 Import/Export via ODBC
        2.4.1 Read from Databases
        2.4.2 Output to and Input from EXCEL Files

3 Data Exploration
    3.1 Have a Look at Data
    3.2 Explore Individual Variables
    3.3 Explore Multiple Variables
    3.4 More Explorations
    3.5 Save Charts into Files

4 Decision Trees and Random Forest
    4.1 Decision Trees with Package party
    4.2 Decision Trees with Package rpart
    4.3 Random Forest

5 Regression
    5.1 Linear Regression
    5.2 Logistic Regression
    5.3 Generalized Linear Regression
    5.4 Non-linear Regression

6 Clustering
    6.1 The k-Means Clustering
    6.2 The k-Medoids Clustering
    6.3 Hierarchical Clustering
    6.4 Density-based Clustering

7 Outlier Detection
    7.1 Univariate Outlier Detection
    7.2 Outlier Detection with LOF
    7.3 Outlier Detection by Clustering
    7.4 Outlier Detection from Time Series
    7.5 Discussions

8 Time Series Analysis and Mining
    8.1 Time Series Data in R
    8.2 Time Series Decomposition
    8.3 Time Series Forecasting
    8.4 Time Series Clustering
        8.4.1 Dynamic Time Warping
        8.4.2 Synthetic Control Chart Time Series Data
        8.4.3 Hierarchical Clustering with Euclidean Distance
        8.4.4 Hierarchical Clustering with DTW Distance
    8.5 Time Series Classification
        8.5.1 Classification with Original Data
        8.5.2 Classification with Extracted Features
        8.5.3 k-NN Classification
    8.6 Discussions
    8.7 Further Readings

9 Association Rules
    9.1 Basics of Association Rules
    9.2 The Titanic Dataset
    9.3 Association Rule Mining
    9.4 Removing Redundancy
    9.5 Interpreting Rules
    9.6 Visualizing Association Rules
    9.7 Discussions and Further Readings

10 Text Mining
    10.1 Retrieving Text from Twitter
    10.2 Transforming Text
    10.3 Stemming Words
    10.4 Building a Term-Document Matrix
    10.5 Frequent Terms and Associations
    10.6 Word Cloud
    10.7 Clustering Words
    10.8 Clustering Tweets
        10.8.1 Clustering Tweets with the k-means Algorithm
        10.8.2 Clustering Tweets with the k-medoids Algorithm
    10.9 Packages, Further Readings and Discussions

11 Social Network Analysis

    11.1 Network of Terms
    11.2 Network of Tweets
    11.3 Two-Mode Network
    11.4 Discussions and Further Readings

12 Case Study I: Analysis and Forecasting of House Price Indices
    12.1 Importing HPI Data
    12.2 Exploration of HPI Data
    12.3 Trend and Seasonal Components of HPI
    12.4 HPI Forecasting
    12.5 The Estimated Price of a Property
    12.6 Discussion

13 Case Study II: Customer Response Prediction and Profit Optimization
    13.1 Introduction
    13.2 The Data of KDD Cup 1998
    13.3 Data Exploration
    13.4 Training Decision Trees
    13.5 Model Evaluation
    13.6 Selecting the Best Tree
    13.7 Scoring
    13.8 Discussions and Conclusions

14 Case Study III: Predictive Modeling of Big Data with Limited Memory
    14.1 Introduction
    14.2 Methodology
    14.3 Data and Variables
    14.4 Random Forest
    14.5 Memory Issue
    14.6 Train Models on Sample Data
    14.7 Build Models with Selected Variables
    14.8 Scoring
    14.9 Print Rules
        14.9.1 Print Rules in Text
        14.9.2 Print Rules for Scoring with SAS
    14.10 Conclusions and Discussion

15 Online Resources
    15.1 R Reference Cards
    15.2 R
    15.3 Data Mining
    15.4 Data Mining with R
    15.5 Classification/Prediction with R
    15.6 Time Series Analysis with R
    15.7 Association Rule Mining with R
    15.8 Spatial Data Analysis with R
    15.9 Text Mining with R
    15.10 Social Network Analysis with R
    15.11 Data Cleansing and Transformation with R
    15.12 Big Data and Parallel Computing with R

About the Author

Dr. Yanchang Zhao is a Senior Data Mining Specialist in Australian public sector. Before joining public sector, he was an Australian Postdoctoral Fellow (Industry) at University of Technology, Sydney from 2007 to 2009. He is the founder of the RDataMining.com website and an RDataMining Group on LinkedIn. He has rich experience in R and data mining. He started his research on data mining since 2001 and has been applying data mining in real-world business applications since 2006. He has over 50 publications on data mining research and applications, including three books. He is a senior member of IEEE, and has been a Program Chair of the Australasian Data Mining Conference (AusDM 2012 & 2013) and a program committee member for more than 50 academic conferences.
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Product Details

  • Hardcover: 256 pages
  • Publisher: Academic Press; 1 edition (December 25, 2012)
  • Language: English
  • ISBN-10: 0123969638
  • ISBN-13: 978-0123969637
  • Product Dimensions: 6 x 0.6 x 9 inches
  • Shipping Weight: 1.3 pounds (View shipping rates and policies)
  • Average Customer Review: 2.2 out of 5 stars  See all reviews (5 customer reviews)
  • Amazon Best Sellers Rank: #931,138 in Books (See Top 100 in Books)

Customer Reviews

Top Customer Reviews

Format: Hardcover
It's not all bad - I really like the R-resources links in Chapter 15, and give points for Chapters 10 and 11, with basic examples of text mining and network analysis, and for the predictive-modeling case study in Chapter 13. (But why do the percentages on page 172 exceed 100?) However, "R and data mining" is not worth anywhere near $70, and as far as substance and quality are concerned, it is one of the weakest books I have seen. On one hand, you are introduced to several useful built-in R functions and "add-on" R packages, including "party" for classification trees, "cluster" and "fpc" for clustering, "arules" for association-rule learning, "tm" for text mining and "igraph" for network visualization. On the other hand, until Chapter 15, there is pretty little value-added - it's as if the author googled a package, and copy-pasted a vignette from the doc. Things are really basic throughout, even where one might expect complexity - Chapter 14 has the most disappointing example. The page count (200+) overstates content, as the book is seriously heavy on whitespace: code and output, hideously typeset, takes up way more space than needed and is often redundant. I do not recommend the purchase, and suggest "Machine learning with R" by Brett Lantz as a better alternative.
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Format: Hardcover
The book is way too pricey for its content and some data in the examples are not even available publicly and need to be purchased separately
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Format: Hardcover Verified Purchase
There are better ones. This is a series of screen shots, which are annotated with some text. An O'Reilly book on R is better if you just want a quick reference, so are many online sites. To learn R look for better ones depending on your level of interest and prior knowledge.
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Format: Hardcover
I have only read a draft copy that the author has / had on his website, and it is a very disappointing book. For example, the content about each data mining method is very sparse, and as one other reviewer noted, with lots of white space, code, and output. Very little comment about how to use the methods in practice. It certainly looks as though for these chapters the author has copy / pasted material from R package documentation. Not worth buying, there is a lot of other material available of much better quality.
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Format: Hardcover Verified Purchase
Personally, I think this is a good book. For expect R users, this is provides an overview. For novice, a good reading material on R and data mining. Obviously, no one book with provide you with everything you want, this adding to to your library will help a lot. Good to have and easy to read.
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