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
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Microsoft Data Mining 2012
1. Introduction to
Microsoft Data Mining
Speaker: William Brown
Microsoft Business Intelligence
September 2012
Mark Ginnebaugh, User Group Leader
mark@designmind.com
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
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
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