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More value from data using Data Mining Allan Mitchell SQL Server MVP Who am I • • • • • • SQL Server MVP SQL Server Consultant Joint author on Wrox Professional SSIS book Worked with SQL Server since version 6.5 www.SQLDTS.com and www.SQLIS.com Partner of SQL Know How Today’s Schedule • • • • what is data mining (Overview) data mining terminology myths around data mining excel AddIn to Office2007 – – – – – Demo Setup Demo Key Influencers Demo Categories Demo Make a Prediction Demo “Other stuff” – if time • Questions and answers What is Data Mining • The process of using statistical techniques to discover subtle relationships between data items, and the construction of predictive models based on them. The process is not the same as just using an OLAP tool to find exceptional items. Generally, data mining is a very different and more specialist application than OLAP, and uses different tools from different vendors. Normally the users are different, too. OLAP vendors have had little success with their data mining efforts. OLAP REPORT What does Data Mining Do? Explores Your Data Finds Patterns Performs Predictions Query, Reporting, Analysis Data Mining What Why How Comparative Benefits Predictive Projects versus Nonpredictive Projects 80% 70% 60% 50% 40% 30% 20% 10% 0% Technology Predictive Productivity Business Process Enhancement Nonpredictive Source: IDC, 2003 Data Mining terminology • • • • • mining structure mining model mining algorithm training dataset testing dataset SQL Server 2005 Algorithms Decision Trees Sequence Clustering Clustering Time Series Association Naïve Bayes Neural Net Plus: Linear and Logistic Regression Sequence Clustering • Applied to – Click stream analysis – Customer segmentation with sequence data – Sequence prediction • Mix of clustering and sequence technologies • Group individuals based on their profiles including sequence data Time Series • Applied to – Forecast sales – Web hits prediction – Stock value estimation • Patented technique from Microsoft Research • Uses regression tree technology to describe and predict series values Clustering • Applied to – Segmentation: Customer grouping, Mailing campaign – Also support classification and regression • Expectation Maximization – Probabilistic Clustering • K-Means – Distance based • Clusters both discrete and continuous values – Discrete values are “binarized” • Anomaly detection • Check variable independence – “Predict Only” attributes not used for clustering Clustering Age Discrete Male Son Daughter Parent Female Clustering Age Anomaly Detection Male Son Daughter Parent Female dm data flow Model Browsing LOB Application Reporting Data Transform (SSIS) Historical Dataset Prediction Mining Models Cube Cube New Dataset the steps to a successful model MS BOL DMX CREATE MINING MODEL CreditRisk INSERT INTO CreditRisk (CustID LONG KEY, Gender TEXT DISCRETE, (CustId, Gender, Income, Profession, Risk) Income LONG CONTINUOUS, Profession TEXT DISCRETE, Risk TEXT DISCRETE PREDICT) Select CustomerID, Gender, Income, Profession,Risk From Customers USING Microsoft_Decision_Trees Select NewCustomers.CustomerID, CreditRisk.Risk, PredictProbability(CreditRisk) FROM CreditRisk PREDICTION JOIN NewCustomers ON CreditRisk.Gender=NewCustomer.Gender AND CreditRisk.Income=NewCustomer.Income AND CreditRisk.Profession=NewCustomer.Profession Myths around data mining • You have to be a propeller head • It’s a new concept. • Only works with SSAS cubes Excel 2007 DMAddin • • • • • DM visualisation table analysis Create session models/permanent models Connect to ssas for full blown models intuitive interface Demos • • • • • setup key Influencers categories Make a prediction other sexy stuff Resources • Loads to be honest (DMX, API to name two things) • Big Subject but very sexy Contact Details [email protected]