Mastering SQL Server 2014 Data Mining -- Table of Contents -- Mastering SQL Server 2014 Data Mining -- Credits -- About the Authors -- About the Reviewers -- www.PacktPub.com -- Support files, eBooks, discount offers, and more -- Why subscribe? -- Free access for Packt account holders -- Instant updates on new Packt books -- Preface -- Data mining - what's the need? -- Information extraction -- Information extraction methodologies -- Data analysis -- Online analytical processing -- Data mining -- Data mining for gaming -- Data mining for business -- Data mining for spatial data -- Data mining for sensor data -- Looking for correlation -- What this book covers -- What you need for this book -- Who this book is for -- Conventions -- Reader feedback -- Customer support -- Downloading the example code -- Downloading the color images of this book -- Errata -- Piracy -- Questions -- 1. Identifying, Staging, and Understanding Data -- Data mining life cycle -- Staging data -- Extract, transform, and load -- Data warehouse -- Measures and dimensions -- Schema -- Data mart -- Refreshing data -- Understanding and cleansing data -- Summary -- 2. Data Model Preparation and Deployment -- Preparing data models -- Cross-Industry Standard Process for Data Mining -- Validating data models -- Preparing the data mining models -- Deploying data models -- Updating the models -- Summary -- 3. Tools of the Trade -- SQL Server BI Suite -- SQL Server Engine -- SQL Server Data Tools -- SQL Server Data Quality Services -- SQL Server Integration Services -- SQL Server Analysis Services -- SQL Server Reporting Services -- References -- Summary -- 4. Preparing the Data -- Listing of popular databases -- Migrating data from popular databases to a staging database -- Migrating data from IBM DB2 -- Building a data warehouse -- Automating data ingestion -- Summary.

5. Classification Models -- Input, output, and predicted columns -- The feature selection -- The Microsoft Decision Tree algorithm -- Data Mining Extensions for the Decision Tree algorithm -- The Microsoft Neural Network algorithm -- Data Mining Extensions for the Neural Network algorithm -- The Microsoft Naïve Bayes algorithm -- Data Mining Extensions for the Naïve Bayes algorithm -- Summary -- 6. Segmentation and Association Models -- The Microsoft Clustering algorithm -- Data Mining Extensions for the Microsoft Clustering models -- The Microsoft Association algorithm -- Data Mining Extensions for the Microsoft Association models -- Summary -- 7. Sequence and Regression Models -- The Microsoft Sequence Clustering algorithm -- Data Mining Extensions for the Microsoft Sequence Clustering models -- The Microsoft Time Series algorithm -- Summary -- 8. Data Mining Using Excel and Big Data -- Data mining using Microsoft Excel -- Data mining using HDInsight and Microsoft Azure Machine Learning -- Microsoft Azure -- Microsoft HDInsight -- HDInsight PowerShell -- Microsoft Azure Machine Learning -- Summary -- 9. Tuning the Models -- Getting the real-world data -- Building the decision tree model -- Tuning the model -- Adding a clustering model to the data mining structure -- Adding the Neural Network model to the data mining structure -- Comparing the predictions of different models -- Summary -- 10. Troubleshooting -- A fraction of rows get transferred into a SQL table -- Error during changing of the data type of the table -- Troubleshooting the data mining structure performance -- The Decision Tree algorithm -- The Naïve Bayes algorithm -- The Microsoft Clustering algorithm -- The Microsoft Association algorithm -- The Microsoft Time Series algorithm -- Error during the deployment of a model -- Summary -- Index.