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Machine Learning with R Cookbook, Second Edition

You're reading from   Machine Learning with R Cookbook, Second Edition Analyze data and build predictive models

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Product type Paperback
Published in Oct 2017
Publisher Packt
ISBN-13 9781787284395
Length 572 pages
Edition 2nd Edition
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Authors (2):
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Ashish Bhatia Ashish Bhatia
Author Profile Icon Ashish Bhatia
Ashish Bhatia
Yu-Wei, Chiu (David Chiu) Yu-Wei, Chiu (David Chiu)
Author Profile Icon Yu-Wei, Chiu (David Chiu)
Yu-Wei, Chiu (David Chiu)
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Table of Contents (15) Chapters Close

Preface 1. Practical Machine Learning with R FREE CHAPTER 2. Data Exploration with Air Quality Datasets 3. Analyzing Time Series Data 4. R and Statistics 5. Understanding Regression Analysis 6. Survival Analysis 7. Classification 1 - Tree, Lazy, and Probabilistic 8. Classification 2 - Neural Network and SVM 9. Model Evaluation 10. Ensemble Learning 11. Clustering 12. Association Analysis and Sequence Mining 13. Dimension Reduction 14. Big Data Analysis (R and Hadoop)

Mining frequent sequential patterns with cSPADE

In contrast to association mining, which only discovers relationships between itemsets, we may be interested in exploring patterns shared among transactions where a set of itemsets occur sequentially.

One of the most famous frequent sequential pattern mining algorithms is the Sequential PAttern Discovery using Equivalence Classes (SPADE) algorithm, which employs the characteristics of a vertical database to perform an intersection on an ID list with an efficient lattice search and allows us to place constraints on mined sequences. In this recipe, we will demonstrate how to use cSPADE to mine frequent sequential patterns.

Getting ready

In this recipe, you have to complete the...

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