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Statistics for Data Science

You're reading from Statistics for Data Science

Product type Book

Published in Nov 2017

Publisher Packt

ISBN-13 9781788290678

Pages 286 pages

Edition 1st Edition

Languages

Concepts

Table of Contents (19) Chapters

Title Page

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. Transitioning from Data Developer to Data Scientist

2. Declaring the Objectives

3. A Developer's Approach to Data Cleaning

4. Data Mining and the Database Developer

5. Statistical Analysis for the Database Developer

6. Database Progression to Database Regression

7. Regularization for Database Improvement

8. Database Development and Assessment

9. Databases and Neural Networks

10. Boosting your Database

11. Database Classification using Support Vector Machines

12. Database Structures and Machine Learning

Sequence mining

Sequence mining evolves the preceding concepts even further. This is a process that the data scientist uses to discover a set of patterns that are shared among objects but which also have between them a specific order.

With sequence mining, we acknowledge that there are sequence rules associated with identified sequences. These rules define the pattern's objects and order. A sequence can have multiple rules. The support of a sequence rule can be calculated or determined by the data scientist by the number of sequences containing the rule divided by the total number of sequences. The confidence of a sequence rule will be the number of sequences containing the rule divided by the number of sequences containing its antecedent.

Overall, the objective of sequential rule mining is to discover all sequential rules having a support and confidence no less than two thresholds, given by the user named minsup and minconf.

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