R and Data Mining - Examples and Case Studies [PDF]

(¯`·._.·[ R and Data Mining - Examples and Case Studies [PDF] ]·._.·´¯)






English | ISBN: 0123969638 | 2013 | 256 pages | PDF | 9 MB


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 R code and data for the book are provided at the RDataMining.com website.

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.

(¯`·._.·[ 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.