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This book guides R users into data mining and helps data miners who use R in their work. It provides a how-to method using R for data mining applications from academia to industry. It
- 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 and studies
The book� helps researchers in the field of data mining, postgraduate students who are interested in data mining, and data miners and analysts from industry. For the many universities that have courses on data mining, this book is an invaluable reference for students studying data mining and its related subjects. In addition, it is a useful resource for anyone involved in industrial training courses on data mining and analytics. The concepts in this book help readers as R becomes increasingly popular for data mining applications.
- Sales Rank: #495352 in Books
- Brand: Brand: Academic Press
- Published on: 2012-12-25
- Original language: English
- Number of items: 1
- Dimensions: 9.02" h x .63" w x 5.98" l, 1.20 pounds
- Binding: Hardcover
- 256 pages
- Used Book in Good Condition
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.
Most helpful customer reviews
13 of 13 people found the following review helpful.
Low-quality and savagely overpriced
By Dimitri Shvorob
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.
UPD. With the benefit of a little more life experience, I would say: don't spend your time on *any* R book. Python is the way to go.
7 of 7 people found the following review helpful.
Pricey and data unavailable
By chrismatic
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
5 of 6 people found the following review helpful.
Not worth buying
By Graham Webster
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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