In the previous chapters, we saw how to use a data mining model to make predictions for a business problem that we framed in Chapter 3, Tools of the Trade. It is worthwhile to take a look at how each of these algorithms are framed, what are their properties, and how the output of these algorithms vary with a change in these properties. A common task in data mining can be categorizing a particular case (categories being one of the many possible outcomes of cases, and case being a group of records corresponding to a single entity). If we take the example of an employee, the categories could be service life, member of leadership team, salary, and so on, while the case would contain the data (attributes) of the current employees such as tenure, salary, employee ID, educational qualification, and so on. The data corresponding to a single employee will be a case, and the data corresponding to many employees will be a case set. We can use the case set to classify...
Mastering SQL Server 2014 Data Mining
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Mastering SQL Server 2014 Data Mining
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Overview of this book
<p>Whether you are new to data mining or are a seasoned expert, this book will provide you with the skills you need to successfully create, customize, and work with Microsoft Data Mining Suite. Starting with the basics, this book will cover how to clean the data, design the problem, and choose a data mining model that will give you the most accurate prediction.</p>
<p>Next, you will be taken through the various classification models such as the decision tree data model, neural network model, as well as Naïve Bayes model. Following this, you'll learn about the clustering and association algorithms, along with the sequencing and regression algorithms, and understand the data mining expressions associated with each algorithm. With ample screenshots that offer a step-by-step account of how to build a data mining solution, this book will ensure your success with this cutting-edge data mining system.</p>
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Table of Contents (17 chapters)
Mastering SQL Server 2014 Data Mining
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Identifying, Staging, and Understanding Data
Data Model Preparation and Deployment
Tools of the Trade
Preparing the Data
Classification Models
Segmentation and Association Models
Sequence and Regression Models
Data Mining Using Excel and Big Data
Tuning the Models
Troubleshooting
Index
Customer Reviews