Recom Systems Provides Business Intelligence solutions to retail industry at a very low price.

1. Recom Retail Solution

14. Dimensional Measure and Aggregation

15. Naïve Bayes

16. Sales Vs Geography Vs Time

17. Customer Vs Geographical Dimension

18. Customer Dimension

19. Geographical Dimension

20. Employee VS Sales Hierarchical Dimension

21. Employee Vs Sales Dimension

22. Setting Bucket Property

23. Product Dimension

24. Time Dimension

25. Relationship Diagram

26. Dimensions and Measure Groups

27. Many to Many Relationships

28. Applying Calculations to Dimensions

29. Yearly Gross Profit Margin on Sales

30. Expanding Product Category

31. Creating Sub Cubes within Multi Dimensional Cubes

32. Key Performance Indicators

33. Time Series Prediction

34. Decision Trees Input column content types Continuous, Cyclical, Discrete, Discretized, Key, Table, and Ordered Predictable column content types Continuous, Cyclical, Discrete, Discretized, Table, and Ordered Modeling flags MODEL_EXISTENCE_ONLY, NOT NULL, and REGRESSOR IsDescendant PredictNodeId IsInNode PredictProbability PredictAdjustedProbability PredictStdev

35. Clustering

36. Market Basket Analysis • Consider shopping cart filled with several items • Market basket analysis tries to answer the following questions: – Who makes purchases? – What do customers buy together? – In what order do customers purchase items? • Given a database of customer transactions, each transaction is a set of items – deduce association rules.

37. Examples of Market Basket Analysis • Co- ocurrences – 80% of all customers purchase items a, b, and c together. • Association Rules – 60% of all customers who purchase X and Y also buy Z. • Sequential Patterns – 60% of customers who first buy X also purchase Y within two weeks. Confidence and Support • We prune the set of all possible association rules using two measures of interest: – Confidence of a rule: X -> Y has confidence c if P( Y| X)= c. – Support of a rule: X-> Y has support s if P( XY)= s. Also, support of an itemset XY.

38. • Direct Marketing • Fraud Detection for Medical Insurance • Floor/ Shelf Planning • Web Site Layout • Cross- selling Applications

39. Frequent Itemsets Applications – Classification – Seeds for constructing Bayesian networks – Web log analysis – Collaborative filtering Association Rules Approaches • Problem Reduction • Breadth- First Search • Depth- First Search

40. Environment Interoperability: key components (client/network/server) work together. Salability: any of the key elements may be replaced when the need to either grow or reduce processing for that element dictates, without major impact on the other elements. Adaptability: new technology (multi-media, broad band networks, distributed database, etc.) may be incorporated into the system. Affordability: using less expensive insures cost effectiveness MISs which available on each platform. Data Integrity: entity, domain and referential integrity are maintained on the database server. Accessibility: data may be accessed from WANs and multiple client applications. Perform: performance may optimize by hardware and process. Security: data security is centralized on the server.

41. Data Warehouse One or more tools to extract fields from any kind of data structure (flat, hierarchical, relational, or object) including external data. The synthesis of the data into a nonvolatile, integrated, subject oriented database with a metadata “catalog.” All AI applications on Data Warehouse

48. Typical Sales Report for Internet Sales Row Labels Internet Order Count Internet Average Sales Amount Internet Average Unit Price Internet Extended Amount Internet Freight Cost CY Q1 6,984 1072.287903 490.9439303 $7,488,858.71 $187,222.15 Accessories 4,619 37.57320416 19.11140073 $173,550.63 $4,339.16 Accessories 4,619 37.57320416 19.11140073 $173,550.63 $4,339.16 Bikes 3,853 1876.266344 1876.266344 $7,229,254.22 $180,731.53 Bikes 3,853 1876.266344 1876.266344 $7,229,254.22 $180,731.53 Clothing 1,930 44.58749223 37.09218103 $86,053.86 $2,151.45 Clothing 1,930 44.58749223 37.09218103 $86,053.86 $2,151.45 CY Q2 8,021 1131.331797 511.7246006 $9,074,412.34 $226,861.10 Accessories 5,171 38.62985496 19.55889357 $199,754.98 $4,994.32 Accessories 5,171 38.62985496 19.55889357 $199,754.98 $4,994.32 Bikes 4,883 1797.313654 1797.313654 $8,776,282.57 $219,407.29 Bikes 4,883 1797.313654 1797.313654 $8,776,282.57 $219,407.29 Clothing 2,170 45.33400461 37.30557072 $98,374.79 $2,459.49 Clothing 2,170 45.33400461 37.30557072 $98,374.79 $2,459.49 CY Q3 5,851 964.884227 451.4985295 $5,645,537.61 $141,138.99 Accessories 3,906 39.02180492 19.56851586 $152,419.17 $3,810.81 Accessories 3,906 39.02180492 19.56851586 $152,419.17 $3,810.81 Bikes 2,774 1953.86977 1953.86977 $5,420,034.74 $135,501.00 Bikes 2,774 1953.86977 1953.86977 $5,420,034.74 $135,501.00 Clothing 1,564 46.72870844 37.65260175 $73,083.70 $1,827.18 Clothing 1,564 46.72870844 37.65260175 $73,083.70 $1,827.18 CY Q4 6,803 1050.987587 479.6316196 $7,149,868.55 $178,747.38 Accessories 4,512 38.79325798 19.42892441 $175,035.18 $4,376.27 Accessories 4,512 38.79325798 19.42892441 $175,035.18 $4,376.27 Bikes 3,695 1865.37838 1865.37838 $6,892,573.11 $172,314.50 Bikes 3,695 1865.37838 1865.37838 $6,892,573.11 $172,314.50 Clothing 1,797 45.77643851 37.34010894 $82,260.26 $2,056.61 Clothing 1,797 45.77643851 37.34010894 $82,260.26 $2,056.61 Grand Total 27,659 1061.451145 486.0869105 $29,358,677.22 $733,969.61

49. Data Mining as an Application Platform

52. Comparative Benefits Predictive Projects versus Nonpredictive Projects

54. What Does Data Mining Do? Explores Your Data Finds Patterns Performs Predictions

55. What does Data Mining do? Illustrated DM Engine DM Engine Predicted Data DB data Client data Application data DB data Client data Application data “ Just one row ” Mining Model Data To Predict Training Data Mining Model Mining Model

56. Server Mining Architecture Analysis Services Server Mining Model Data Mining Algorithm Data Source Your Application OLE DB/ ADOMD/ XMLA Deploy BI Dev Studio (Visual Studio) App Data

57. Data Mining Process CRISP-DM “ Putting Data Mining to Work” “ Doing Data Mining” Data www.crisp-dm.org Business Understanding Data Understanding Data Preparation Modeling Evaluation Deployment

58. Data Mining Process in SQL CRISP-DM SSAS (Data Mining) SSAS (OLAP) DSV SSIS SSAS(OLAP) SSRS Flexible APIs SSIS SSAS (OLAP) Data www.crisp-dm.org Data Business Understanding Data Understanding Data Preparation Modeling Evaluation Deployment

59. What Do Data Mining Applications Do? Finds Patterns Performs Predictions Explores Your Data Automatic Mining Pattern Exploration Perform Predictions

60. Algorithm Training Algorithm Module Case Processor (generates and prepares all training cases) StartCases Process One Case Converged/complete? No Yes Done! Persist patterns

61. DM data flow New Dataset Cube Historical Dataset Data Transform (DTS) Reporting Mining Models Model Browsing Prediction LOB Application Cube

62. Prediction Parser Validation-I & Initialization AST Binding & Validation-II DMX tree Execution Planning DMX tree Input data Read / Evaluate one row Push response Untokenize results Income Gender $50,000 F 1 2 50000 2 1 2 3 50000 2 1 Income Gender Plan $50,000 F Attend