SQL Server 2012 Tutorials:
Analysis Services - Data Mining
SQL Server 2012 Books Online
Summary: Microsoft SQL Server Analysis Services makes it easy to create sophisticated data mining solutions. The step-by-step tutorials in the following list will help you learn how to get the most out of Analysis Services, so that you can perform advanced analysis to solve business problems that are beyond the reach of traditional business intelligence methods.
Category: Step-by-Step
Applies to: SQL Server 2012
Source: SQL Server Books Online (link to source content)
E-book publication date: June 2012
Copyright © 2012 by Microsoft Corporation
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Contents |
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Data Mining Tutorials (Analysis Services) .............................................................................................................. |
5 |
Basic Data Mining Tutorial........................................................................................................................................... |
6 |
Lesson 1: Preparing the Analysis Services Database (Basic Data Mining Tutorial)............................. |
8 |
Creating an Analysis Services Project (Basic Data Mining Tutorial) ..................................................... |
9 |
Creating a Data Source (Basic Data Mining Tutorial) ............................................................................. |
10 |
Creating a Data Source View (Basic Data Mining Tutorial) .................................................................. |
11 |
Lesson 2: Building a Targeted Mailing Structure (Basic Data Mining Tutorial)................................. |
12 |
Creating a Targeted Mailing Mining Model Structure (Basic Data Mining Tutorial).................. |
13 |
Specifying the Data Type and Content Type (Basic Data Mining Tutorial) .................................... |
16 |
Specifying a Testing Data Set for the Structure (Basic Data Mining Tutorial)............................... |
17 |
Lesson 3: Adding and Processing Models ...................................................................................................... |
18 |
Adding New Models to the Targeted Mailing Structure (Basic Data Mining Tutorial).............. |
19 |
Processing Models in the Targeted Mailing Structure (Basic Data Mining Tutorial).................. |
20 |
Lesson 4: Exploring the Targeted Mailing Models (Basic Data Mining Tutorial) ............................. |
22 |
Exploring the Decision Tree Model (Basic Data Mining Tutorial) ...................................................... |
23 |
Exploring the Clustering Model (Basic Data Mining Tutorial)............................................................. |
25 |
Exploring the Naive Bayes Model (Basic Data Mining Tutorial) ......................................................... |
28 |
Lesson 5: Testing Models (Basic Data Mining Tutorial) ............................................................................. |
30 |
Testing Accuracy with Lift Charts (Basic Data Mining Tutorial) .......................................................... |
31 |
Testing a Filtered Model (Basic Data Mining Tutorial)........................................................................... |
33 |
Lesson 6: Creating and Working with Predictions (Basic Data Mining Tutorial).............................. |
36 |
Creating Predictions (Basic Data Mining Tutorial)................................................................................... |
36 |
Using Drillthrough on Structure Data (Basic Data Mining Tutorial).................................................. |
40 |
Intermediate Data Mining Tutorial (Analysis Services - Data Mining)..................................................... |
42 |
Lesson 1: Creating the Intermediate Data Mining Solution (Intermediate Data Mining Tutorial) |
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..................................................................................................................................................................................... |
44 |
Creating a Solution and Data Source (Intermediate Data Mining Tutorial)................................... |
44 |
Lesson 2: Building a Forecasting Scenario (Intermediate Data Mining Tutorial) ............................. |
47 |
Adding a Data Source View for Forecasting (Intermediate Data Mining Tutorial)...................... |
48 |
Understanding the Requirements for a Time Series Model (Intermediate Data Mining |
|
Tutorial) ............................................................................................................................................................ |
49 |
Creating a Forecasting Structure and Model (Intermediate Data Mining Tutorial).................... |
52 |
Modifying the Forecasting Structure (Intermediate Data Mining Tutorial).................................... |
53 |
Customizing and Processing the Forecasting Model (Intermediate Data Mining Tutorial)..... |
54 |
Exploring the Forecasting Model (Intermediate Data Mining Tutorial)........................................... |
57 |
Creating Time Series Predictions (Intermediate Data Mining Tutorial)........................................... |
62 |
Advanced Time Series Predictions (Intermediate Data Mining Tutorial) ........................................ |
67 |
Time Series Predictions using Updated Data (Intermediate Data Mining Tutorial)................ |
71 |
Time Series Predictions using Replacement Data (Intermediate Data Mining Tutorial) ....... |
73 |
Comparing Predictions for Forecasting Models (Intermediate Data Mining Tutorial) .......... |
77 |
Lesson 3: Building a Market Basket Scenario (Intermediate Data Mining Tutorial)........................ |
80 |
Adding a Data Source View with Nested Tables (Intermediate Data Mining Tutorial) ............. |
81 |
Creating a Market Basket Structure and Model (Intermediate Data Mining Tutorial)............... |
83 |
Modifying and Processing the Market Basket Model (Intermediate Data Mining Tutorial).... |
86 |
Exploring the Market Basket Models (Intermediate Data Mining Tutorial) ................................... |
87 |
Filtering a Nested Table in a Mining Model (Intermediate Data Mining Tutorial)...................... |
92 |
Predicting Associations (Intermediate Data Mining Tutorial) ............................................................. |
95 |
Lesson 4: Building a Sequence Clustering Scenario (Intermediate Data Mining Tutorial)......... |
100 |
Creating a Sequence Clustering Mining Model Structure (Intermediate Data Mining Tutorial) |
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............................................................................................................................................................................... |
101 |
Processing the Sequence Clustering Model ............................................................................................ |
104 |
Exploring the Sequence Clustering Model (Intermediate Data Mining Tutorial)....................... |
104 |
Creating a Related Sequence Clustering Model (Intermediate Data Mining Tutorial)............ |
112 |
Creating Predictions on a Sequence Clustering Model (Intermediate Data Mining Tutorial) |
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............................................................................................................................................................................... |
113 |
Lesson 5: Building Neural Network and Logistic Regression Models (Intermediate Data Mining |
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Tutorial).................................................................................................................................................................. |
119 |
Adding a Data Source View for Call Center Data (Intermediate Data Mining Tutorial) .......... |
120 |
Creating a Neural Network Structure and Model (Intermediate Data Mining Tutorial) ......... |
123 |
Exploring the Call Center Model (Intermediate Data Mining Tutorial).......................................... |
133 |
Adding a Logistic Regression Model to the Call Center Structure (Intermediate Data Mining |
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Tutorial).............................................................................................................................................................. |
138 |
Creating Predictions for the Call Center Models (Intermediate Data Mining Tutorial) ........... |
140 |
Creating and Querying Data Mining Models with DMX: Tutorials (Analysis Services - Data |
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Mining)....................................................................................................................................................................... |
145 |
Bike Buyer DMX Tutorial...................................................................................................................................... |
147 |
Lesson 1: Creating the Bike Buyer Mining Structure............................................................................. |
150 |
Lesson 2: Adding Mining Models to the Bike Buyer Mining Structure.......................................... |
154 |
Lesson 3: Processing the Bike Buyer Mining Structure........................................................................ |
158 |
Lesson 4: Browsing the Bike Buyer Mining Models............................................................................... |
162 |
Lesson 5: Executing Prediction Queries ..................................................................................................... |
166 |
Market Basket DMX Tutorial.............................................................................................................................. |
173 |
Lesson 1: Creating the Market Basket Mining Structure..................................................................... |
176 |
Lesson 2: Adding Mining Models to the Market Basket Mining Structure .................................. |
179 |
Lesson 3: Processing the Market Basket Mining Structure................................................................. |
185 |
Lesson 4: Executing Market Basket Predictions...................................................................................... |
189 |
Time Series Prediction DMX Tutorial.............................................................................................................. |
194 |
Lesson 1: Creating a Time Series Mining Model and Mining Structure ........................................ |
195 |
Lesson 2: Adding Mining Models to the Time Series Mining Structure........................................ |
199 |
Lesson 3: Processing the Time Series Structure and Models............................................................. |
203 |
Lesson 4: Creating Time Series Predictions Using DMX...................................................................... |
206 |
Lesson 5: Extending the Time Series Model............................................................................................. |
208 |
Data Mining Tutorials (Analysis Services)
Microsoft SQL Server Analysis Services makes it easy to create sophisticated data mining solutions. The tools in Analysis Services help you design, create, and manage data mining models that use either relational or cube data. You can manage client access to data mining models and create prediction queries from multiple clients.
The step-by-step tutorials in the following list will help you learn how to get the most out of Analysis Services, so that you can perform advanced analysis to solve business problems that are beyond the reach of traditional business intelligence methods.
In this Section
•Basic Data Mining Tutorial
This tutorial walks you through a targeted mailing scenario. It demonstrates how to use the data mining algorithms, mining model viewers, and data mining tools that are included in Analysis Services. You will build three data mining models to answer practical business questions while learning data mining concepts and tools.
•Intermediate Data Mining Tutorial (Analysis Services - Data Mining)
This tutorial contains a collection of lessons that introduce more advanced data mining concepts and techniques. The scenarios include these model types:
•forecasting
•market basket analysis
•neural networks and logistic regression
•sequence clustering
The lessons are independent and can be done in any order, but you should have a basic knowledge of how to build data sources.
Advanced concepts covered in these lessons include the use of nested tables, crossprediction, custom data source views and named queries, and filtering in data mining queries. You will also gain proficiency in using the prediction query tools that are included in Analysis Services.
Reference
Data Mining Algorithms (Analysis Services - Data Mining)
Data Mining Extensions (DMX) Reference
Related Sections
Using the Data Mining Tools
Logical Architecture (Analysis Services - Data Mining)
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