Data Mining for
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1 Data Mining for
2 Data Mining? Predictive Analytics? Knowledge Discovery?
3
4 Existing data Apply Patterns Find and Refine Patterns New Data
5 SQL Server Data Mining Tools SQL Server Standard & Enterprise Microsoft Office Excel + Data Mining Add-in Microsoft Office Excel + Table Analysis Add-in SQL Server Cloud Data Mining SQL Server Cloud Data Mining Excel Add-in
6 Demo Data Mining tools in action
7
8 Mining Model
9 Training Data DB data application data Data Mining Engine Mining Model Mining Model
10 Training Data DB data application data Mining Model New Data DB data application data Just one row Data Mining Engine Data Mining Engine Mining Model Mining Model Predicted Data
11 SQL Server Data Mining Architecture BI Dev Studio Client Application OLE DB/ ADOMD.Net / XMLA App Data Deploy Analysis Services Server Mining Model Data Mining Algorithm Data Source
12 Training Data DB data application data Mining Model New Data DB data application data Just one row Data Mining Engine Data Mining Engine Mining Model Mining Model Predicted Data
13 Data Mining Extensions to SQL (DMX) CREATE MINING MODEL CreditRisk (CustID LONG KEY, Gender TEXT DISCRETE, Income LONG CONTINUOUS, Profession TEXT DISCRETE, Risk TEXT DISCRETE PREDICT) USING Microsoft_Decision_Trees
14 Data Mining Extensions to SQL (DMX) CREATE MINING MODEL CreditRisk (CustID LONG KEY, Gender TEXT DISCRETE, Income LONG CONTINUOUS, Profession TEXT DISCRETE, Risk TEXT DISCRETE PREDICT) USING Microsoft_Decision_Trees INSERT INTO CreditRisk (CustId, Gender, Income, Profession, Risk) Select CustomerID, Gender, Income, Profession,Risk From Customers
15 Data Mining Extensions to SQL (DMX) CREATE MINING MODEL CreditRisk (CustID LONG KEY, Gender TEXT DISCRETE, Income LONG CONTINUOUS, Profession TEXT DISCRETE, Risk TEXT DISCRETE PREDICT) USING Microsoft_Decision_Trees INSERT INTO CreditRisk (CustId, Gender, Income, Profession, Risk) Select CustomerID, Gender, Income, Profession,Risk From Customers Select NewCustomers.CustomerID, CreditRisk.Risk, PredictProbability(CreditRisk.Risk) FROM CreditRisk PREDICTION JOIN NewCustomers ON CreditRisk.Gender=NewCustomer.Gender AND CreditRisk.Income=NewCustomer.Income AND CreditRisk.Profession=NewCustomer.Profession
16 Data Mining Extensions to SQL (DMX) CREATE MINING MODEL CreditRisk (CustID LONG KEY, Gender TEXT DISCRETE, Income LONG CONTINUOUS, Profession TEXT DISCRETE, Risk TEXT DISCRETE PREDICT) USING Microsoft_Decision_Trees INSERT INTO CreditRisk (CustId, Gender, Income, Profession, Risk) Select CustomerID, Gender, Income, Profession,Risk From Customers Select NewCustomers.CustomerID, CreditRisk.Risk, PredictProbability(CreditRisk.Risk) FROM CreditRisk NATURAL PREDICTION JOIN NewCustomers
17 The Five Elements of Success Right problem Right criteria Right data Right results Right delivery
18 Data, or rules?
19 Data, or rules? What you know, or what you don t know?
20 Traditional BI Exploration Drill down Trending Force constraints Apply rules & models Predictive Analysis Discovery Classification Prediction Discover outliers Find patterns & relationships
21 Your Biggest Job GI-GO
22 Clean Your Data Remove duplicates and missing values Remove out-of-range data
23 Clarify Your Data True, False and NULL boolean fields Remove synonyms UnitPrice * Qty = TotalPrice Calculate derived values
24 Simplify Your Data Bucket or Group continuous or many-valued columns
25 Demo The importance of data preparation
26 Validation
27 All models are wrong but some are useful George Box
28 How will you measure success? Technical: Lift and accuracy Business: ROI and KPI
29 Good Criteria Relevant and actionable Strategic vs. operational relevance Realistic and achievable
30 Demo Validating Mining Models
31 The Five Elements of Success Right problem Right criteria Right data Right results Right delivery
32 Demo Putting it together by Mining SQL Server management data
33 For More Information
34 Additional Resources
35
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