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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

Published in: Technology
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Microsoft Data Mining 2012

  1. 1. Introduction to Microsoft Data Mining  Speaker: William Brown Microsoft Business Intelligence September 2012 Mark Ginnebaugh, User Group Leader
  2. 2. ObjectivesWilliam Brown, Microsoft BI Architect September 2012
  3. 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. 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. 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 Work” William Brown, Microsoft BI Architect
  6. 6. Data Preparation William Brown, Microsoft BI Architect
  7. 7. Data Mining ModelingDesign timeProcess timeQuery time Mining Model William Brown, Microsoft BI Architect
  8. 8. Data Mining ModelingDesign timeProcess timeQuery time Mining Model Data Mining Engine Training Data William Brown, Microsoft BI Architect
  9. 9. Data Mining ModelingDesign timeProcess timeQuery time Mining Model Data Mining Engine Predicted Data to Data Predict William Brown, Microsoft BI Architect
  10. 10. Introducing Analysis Services 2012 William Brown, Microsoft BI Architect
  11. 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. 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. 13. SQL Server Data Mining Algorithms William Brown, Microsoft BI Architect
  14. 14. SQL Server Data Mining Algorithms Continued William Brown, Microsoft BI Architect
  15. 15. SQL Server Data Mining Algorithms Continued William Brown, Microsoft BI Architect
  16. 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. 17. Data Mining Add‐Ins for ExcelMenu Data miningAnalyze Key Influencers Naïve BayesDetect Categories ClusteringFill from Example Logical RegressionForecast Time SeriesHighlight Exceptions ClusteringScenario Analysis – Goal Seek Logical RegressionScenario Analysis – What if Logical RegressionPredicton Calculator Logical RegressionShopping Basket Association Rules William Brown, Microsoft BI Architect
  18. 18. SQL Server Data Mining Visualizations William Brown, Microsoft BI Architect
  19. 19. 1. Creating, training, testing data mining models with SSDT2. Using Excel for user driven data mining3. Authoring a Reporting Services report based on a data mining  model4. Automating data validation with data mining
  20. 20. Message for Developers William Brown, Microsoft BI Architect
  21. 21. Technical Resources‐technologies/business‐intelligence/data‐mining.aspx William Brown, Microsoft BI Architect September 2012
  22. 22. To learn more or inquire about speaking opportunities, please contact: Mark Ginnebaugh, User Group Leader