In addition to the Apriori algorithm, you can use the Eclat algorithm to generate frequent itemsets. As the Apriori algorithm performs a breadth-first search to scan the complete database, the support counting is rather time consuming. Alternatively, if the database fits into the memory, you can use the Eclat algorithm, which performs a depth-first search to count the supports. The Eclat algorithm, therefore, performs quicker than the Apriori algorithm. In this recipe, we will introduce how to use the Eclat algorithm to generate frequent itemsets.
Machine Learning with R Cookbook, Second Edition - Second Edition
By :
Machine Learning with R Cookbook, Second Edition - Second Edition
By:
Overview of this book
Big data has become a popular buzzword across many industries. An increasing number of people have been exposed to the term and are looking at how to leverage big data in their own businesses, to improve sales and profitability. However, collecting, aggregating, and visualizing data is just one part of the equation. Being able to extract useful information from data is another task, and a much more challenging one. Machine Learning with R Cookbook, Second Edition uses a practical approach to teach you how to perform machine learning with R. Each chapter is divided into several simple recipes. Through the step-by-step instructions provided in each recipe, you will be able to construct a predictive model by using a variety of machine learning packages. In this book, you will first learn to set up the R environment and use simple R commands to explore data. The next topic covers how to perform statistical analysis with machine learning analysis and assess created models, covered in detail later on in the book. You'll also learn how to integrate R and Hadoop to create a big data analysis platform. The detailed illustrations provide all the information required to start applying machine learning to individual projects. With Machine Learning with R Cookbook, machine learning has never been easier.
Table of Contents (21 chapters)
Title Page
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Free Chapter
Practical Machine Learning with R
Data Exploration with Air Quality Datasets
Analyzing Time Series Data
R and Statistics
Understanding Regression Analysis
Survival Analysis
Classification 1 - Tree, Lazy, and Probabilistic
Classification 2 - Neural Network and SVM
Model Evaluation
Ensemble Learning
Clustering
Association Analysis and Sequence Mining
Dimension Reduction
Big Data Analysis (R and Hadoop)
Customer Reviews