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You're reading from Practical Machine Learning Learn how to build Machine Learning applications to solve real-world data analysis challenges with this Machine Learning book – packed with practical tutorials

Product type Paperback

Published in Jan 2016

Publisher Packt

ISBN-13 9781784399689

Length 468 pages

Edition 1st Edition

Languages

Python

Tools

Apache Spark

Concepts

Machine Learning

Author (1):

Sunila Gollapudi

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Table of Contents (16) Chapters

Preface

1. Introduction to Machine learning FREE CHAPTER

2. Machine learning and Large-scale datasets

3. An Introduction to Hadoop's Architecture and Ecosystem

4. Machine Learning Tools, Libraries, and Frameworks

5. Decision Tree based learning

6. Instance and Kernel Methods Based Learning

7. Association Rules based learning

8. Clustering based learning

9. Bayesian learning

10. Regression based learning

11. Deep learning

12. Reinforcement learning

13. Ensemble learning

14. New generation data architectures for Machine learning

Index

Association rules based learning

Association rule-based Machine learning deals with finding frequent patterns, associations, and transactions that can be used for classification and prediction requirements. The association rule based learning process is as follows: given a set of transactions, finding rules and using these rules to predict the occurrence of an item based on the occurrences of other items in the transaction is Association rule based learning. The following diagram represents the scope of Machine learning:

Association rule – a definition

An association rule is a representation of a pattern that describes the probability with which an event occurs, given the occurrence of another event. Usually, the syntax for association rules follows the if...then statements that relate two sets of unrelated data from the repository. In short, it helps find the relationship between objects that are frequently used together. The goal of association rules is to find all the sets of items that...