Microsoft Data Mining 2012
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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
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Microsoft Data Mining 2012
- 1. Introduction to Microsoft Data Mining Speaker: William Brown Microsoft Business Intelligence September 2012 Mark Ginnebaugh, User Group Leader [email protected]
- 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
- 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 [email protected]