Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Practical Predictive Analytics

You're reading from   Practical Predictive Analytics Analyse current and historical data to predict future trends using R, Spark, and more

Arrow left icon
Product type Paperback
Published in Jun 2017
Publisher Packt
ISBN-13 9781785886188
Length 576 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Ralph Winters Ralph Winters
Author Profile Icon Ralph Winters
Ralph Winters
Arrow right icon
View More author details
Toc

Table of Contents (13) Chapters Close

Preface 1. Getting Started with Predictive Analytics FREE CHAPTER 2. The Modeling Process 3. Inputting and Exploring Data 4. Introduction to Regression Algorithms 5. Introduction to Decision Trees, Clustering, and SVM 6. Using Survival Analysis to Predict and Analyze Customer Churn 7. Using Market Basket Analysis as a Recommender Engine 8. Exploring Health Care Enrollment Data as a Time Series 9. Introduction to Spark Using R 10. Exploring Large Datasets Using Spark 11. Spark Machine Learning - Regression and Cluster Models 12. Spark Models – Rule-Based Learning

Association rule algorithms


Without an association rule algorithm, you are left with the computationally very expensive task of generating all possible pairs of itemsets, and then trying to mine the data in order to identify the best ones yourself. Associate rule algorithms help with filtering this.

The most popular algorithm for MBA is the apriori algorithm, which is contained within the arules package (the other popular algorithm is the eclat algorithm).

Running apriori is fairly simple. We will demonstrate this using our demo 10 transaction itemset that we just printed.

The apriori algorithm is based upon the principle that if a particular itemset is frequent, then all of its subsets must also be frequent. That principle itself is helpful for reducing the number of itemsets that need to be evaluated, since it only needs to look at the largest items sets first, and then be able to filter down:

  • First, some housekeeping. Fix the number of printable digits to 2:
         options(digits = 2)
  • Next...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €18.99/month. Cancel anytime