Author: William Brown, Microsoft BI Specialist > This slide presentation covers Microsoft Data Mining functionality from the developer to the end user. In the past, data mining belonged to the deep technical specialist, but the current Microsoft stack allows anyone to create very powerful data mining models. Data mining allows users to find insights that are difficult or impossible to discover with traditional analysis. You'll learn * How to get started with Data mining * The various data mining models and where they can be applied * How to create models and surface the data to users * How to use the new Excel Data mining add-in

1. Introduction to
Microsoft Data Mining
Speaker: William Brown
Microsoft Business Intelligence
September 2012
Mark Ginnebaugh, User Group Leader
mark@designmind.com

2. Objectives
William Brown, Microsoft BI Architect September 2012

3. Data Mining is….
Data mining is the locating of previously unknown patterns and relationships within
data using a database application
Identify and
Locate and handle
Identify Cross Understand Forecast sales
understand anomalies
Selling and predict and inventory
profitable during data
Opportunities fraud data
customers transfer or
data loading
William Brown, Microsoft BI Architect September 2012

4. Data Mining does not….
Data mining is the locating of previously unknown patterns and relationships within
data using a database application
Reduce the
Remove your Find simple
work to Reduce the Magically
need to answers to
prepare and impact of make your life
understand complex
organize the dirty data easier 
your data! questions
data
William Brown, Microsoft BI Architect

5. Describing the Data Mining Process
“Doing Data
Business Data Mining”
Understanding Understanding
Data
Preparation
Data
Deployment
Modeling
“Putting
Data
“Putting Data
Mining
Mining to
to Work”
Evaluation www.crisp-dm.org
Work”
William Brown, Microsoft BI Architect

6. Data Preparation
William Brown, Microsoft BI Architect

7. Data Mining Modeling
Design time
Process time
Query time Mining
Model
William Brown, Microsoft BI Architect

8. Data Mining Modeling
Design time
Process time
Query time Mining
Model
Data
Mining
Engine
Training
Data
William Brown, Microsoft BI Architect

9. Data Mining Modeling
Design time
Process time
Query time Mining
Model
Data
Mining
Engine
Predicted Data to
Data Predict
William Brown, Microsoft BI Architect

10. Introducing Analysis Services 2012
William Brown, Microsoft BI Architect

11. Intro to SQL Server Data Mining
 Hides the complexity
 Includes full suite of algorithms to automatically
identify and store patterns in your data
William Brown, Microsoft BI Architect

12. Data Mining Add‐Ins for Excel
 Free add‐in for Excel 2010
 Works with 32 and 64 bit editions of Office 2010
 Requires SQL Server Analysis Services
 Analyze Tab – simpler to use
 Data Mining Tab – full power
William Brown, Microsoft BI Architect

13. SQL Server Data Mining Algorithms
William Brown, Microsoft BI Architect

14. SQL Server Data Mining Algorithms
Continued
William Brown, Microsoft BI Architect

15. SQL Server Data Mining Algorithms
Continued
William Brown, Microsoft BI Architect

16. SQL Server Data Mining Algorithms
Continued
Classify Estimate Cluster Forecast Associate
• Decision • Decision • Clustering • Time Series • Association
Trees Trees Rules
• Logistic • Linear • Decision
Regression Regression Trees
• Naïve • Logistic
Bayes Regression
• Neural • Neural
Networks Networks
William Brown, Microsoft BI Architect

17. Data Mining Add‐Ins for Excel
Menu Data mining
Analyze Key Influencers Naïve Bayes
Detect Categories Clustering
Fill from Example Logical Regression
Forecast Time Series
Highlight Exceptions Clustering
Scenario Analysis – Goal Seek Logical Regression
Scenario Analysis – What if Logical Regression
Predicton Calculator Logical Regression
Shopping Basket Association Rules
William Brown, Microsoft BI Architect

18. SQL Server Data Mining Visualizations
William Brown, Microsoft BI Architect

19. 1. Creating, training, testing data mining models with SSDT
2. Using Excel for user driven data mining
3. Authoring a Reporting Services report based on a data mining
model
4. Automating data validation with data mining

20. Message for Developers
William Brown, Microsoft BI Architect

21. Technical Resources
http://www.microsoft.com/sqlserver/en/us/solutions‐technologies/business‐intelligence/data‐mining.aspx
http://www.sqlserverdatamining.com
http://www.predixionsoftware.com/predixion/
William Brown, Microsoft BI Architect September 2012

22. To learn more or inquire about speaking opportunities, please contact:
Mark Ginnebaugh, User Group Leader
mark@designmind.com