Book Image

Learning Data Mining with R

By : Bater Makhabel
Book Image

Learning Data Mining with R

By: Bater Makhabel

Overview of this book

<p>Being able to deal with the array of problems that you may encounter during complex statistical projects can be difficult. If you have only a basic knowledge of R, this book will provide you with the skills and knowledge to successfully create and customize the most popular data mining algorithms to overcome these difficulties.</p> <p>You will learn how to manipulate data with R using code snippets and be introduced to mining frequent patterns, association, and correlations while working with R programs. Discover how to write code for various predication models, stream data, and time-series data. You will also be introduced to solutions written in R based on RHadoop projects. You will finish this book feeling confident in your ability to know which data mining algorithm to apply in any situation.</p>

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Learning Data Mining with R
Bater Makhabel
Jan, 2015 | 314 pages
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Table of Contents (19 chapters)
Learning Data Mining with R
Learning Data Mining with R
Credits
Credits
About the Author
About the Author
Acknowledgments
Acknowledgments
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Warming Up
Warming Up
Big data
Data source
Data mining
Social network mining
Text mining
Web data mining
Why R?
Statistics
Machine learning
Data attributes and description
Data cleaning
Data integration
Data dimension reduction
Data transformation and discretization
Visualization of results
Time for action
Summary
2
Mining Frequent Patterns, Associations, and Correlations
Mining Frequent Patterns, Associations, and Correlations
An overview of associations and patterns
Market basket analysis
Hybrid association rules mining
Mining sequence dataset
The R implementation
High-performance algorithms
Time for action
Summary
3
Classification
Classification
Classification
Generic decision tree induction
High-value credit card customers classification using ID3
Web spam detection using C4.5
Web key resource page judgment using CART
Trojan traffic identification method and Bayes classification
Identify spam e-mail and Naïve Bayes classification
Rule-based classification of player types in computer games and rule-based classification
Time for action
Summary
4
Advanced Classification
Advanced Classification
Ensemble (EM) methods
Biological traits and the Bayesian belief network
Protein classification and the k-Nearest Neighbors algorithm
Document retrieval and Support Vector Machine
Classification using frequent patterns
Classification using the backpropagation algorithm
Time for action
Summary
5
Cluster Analysis
Cluster Analysis
Search engines and the k-means algorithm
Automatic abstraction of document texts and the k-medoids algorithm
The CLARA algorithm
CLARANS
Unsupervised image categorization and affinity propagation clustering
News categorization and hierarchical clustering
Time for action
Summary
6
Advanced Cluster Analysis
Advanced Cluster Analysis
Customer categorization analysis of e-commerce and DBSCAN
Clustering web pages and OPTICS
Visitor analysis in the browser cache and DENCLUE
Recommendation system and STING
Web sentiment analysis and CLIQUE
Opinion mining and WAVE clustering
User search intent and the EM algorithm
Customer purchase data analysis and clustering high-dimensional data
SNS and clustering graph and network data
Time for action
Summary
7
Outlier Detection
Outlier Detection
Credit card fraud detection and statistical methods
Activity monitoring – the detection of fraud involving mobile phones and proximity-based methods
Intrusion detection and density-based methods
Intrusion detection and clustering-based methods
Monitoring the performance of the web server and classification-based methods
Detecting novelty in text, topic detection, and mining contextual outliers
Collective outliers on spatial data
Outlier detection in high-dimensional data
Time for action
Summary
8
Mining Stream, Time-series, and Sequence Data
Mining Stream, Time-series, and Sequence Data
The credit card transaction flow and STREAM algorithm
Predicting future prices and time-series analysis
Stock market data and time-series clustering and classification
Web click streams and mining symbolic sequences
Mining sequence patterns in transactional databases
Time for action
Summary
9
Graph Mining and Network Analysis
Graph Mining and Network Analysis
Graph mining
Mining frequent subgraph patterns
Social network mining
Time for action
Summary
10
Mining Text and Web Data
Mining Text and Web Data
Text mining and TM packages
Text summarization
The question answering system
Genre categorization of web pages
Categorizing newspaper articles and newswires into topics
Web usage mining with web logs
Time for action
Summary
Algorithms and Data Structures
Algorithms and Data Structures
Index
Index

Classification using the backpropagation algorithm

The backpropagation (BP) algorithm learns the classification model by training a multilayer feed-forward neural network. The generic architecture of the neural network for BP is shown in the following diagrams, with one input layer, some hidden layers, and one output layer. Each layer contains some units or perceptron. Each unit might be linked to others by weighted connections. The values of the weights are initialized before the training. The number of units in each layer, number of hidden layers, and the connections will be empirically defined at the very start.

The training tuples are assigned to the input layer; each unit in the input layer calculates the result with certain functions and the input attributes from the training tuple, and then the output is served as the input parameter for the hidden layer; the calculation here happened layer by layer. As a consequence, the output of the network contains all the output of each unit in...

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