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sumit621
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Data Warehouses Data Mining Business Intelligence Applications
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Why needed? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Why needed? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
What Is Data Mining? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
DB  DW  DM == Knowledge Discovery in DB ,[object Object],Data Cleaning Data Integration Databases Data Warehouse Knowledge Task-relevant Data Selection Data Mining Pattern Evaluation
Steps of a KDD Process ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Mining and Business Intelligence Increasing potential to support business decisions End User Business Analyst Data Analyst DBA Making Decisions Data Presentation Visualization Techniques Data Mining Information Discovery Data Exploration OLAP, MDA Statistical Analysis, Querying and Reporting Data Warehouses / Data Marts Data Sources Paper, Files, Information Providers, Database Systems, OLTP
Data Warehouses: Data Cube  OLAP all Europe North_America Mexico Canada Spain Germany Vancouver M. Wind L. Chan ... ... ... ... ... ... all region office country Toronto Frankfurt city
Data Warehouses: Data Cube  OLAP Product Region Month Dimensions: Product, Location, Time Hierarchical summarization paths Industry Region Year Category Country Quarter Product City Month Week Office Day
Data Warehouses: Data Cube  OLAP Total annual sales of TV in U.S.A. Date Product Country All, All, All sum sum TV VCR PC 1Qtr 2Qtr 3Qtr 4Qtr U.S.A Canada Mexico sum
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Data Mining Functionalities (1)
Data Mining Functionalities (2) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Data Mining Functionalities (3)
Are All the “Discovered” Patterns Interesting? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Can We Find All and Only Interesting Patterns? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Preprocessing Why Data Preprocessing? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Major Tasks in Data Preprocessing ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Cleaning ,[object Object],[object Object],[object Object],[object Object]
How to Handle Missing Data? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
How to Handle Noisy Data? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Integration ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Transformation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Reduction Strategies ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Mining Association Rules in Large Databases ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
What Is Association Mining? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Rule Measures: Support and Confidence ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Customer buys diaper Customer buys both Customer buys beer
Association Rule Mining ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Concept Description: Characterization and Comparison ,[object Object],[object Object],[object Object]
Classification and Prediction ,[object Object],[object Object],[object Object],[object Object],[object Object]
Classification—A Two-Step Process ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Classification Process (1): Model Construction Training Data Classification Algorithms IF rank = ‘professor’ OR years > 6 THEN tenured = ‘yes’ Classifier (Model)
Classification Process (2): Use the Model in Prediction Classifier Testing Data Unseen Data (Jeff, Professor, 4) Tenured?
Supervised vs. Unsupervised Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Q and A Thank you !!!

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Cssu dw dm

  • 1. Data Warehouses Data Mining Business Intelligence Applications
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8. Data Mining and Business Intelligence Increasing potential to support business decisions End User Business Analyst Data Analyst DBA Making Decisions Data Presentation Visualization Techniques Data Mining Information Discovery Data Exploration OLAP, MDA Statistical Analysis, Querying and Reporting Data Warehouses / Data Marts Data Sources Paper, Files, Information Providers, Database Systems, OLTP
  • 9. Data Warehouses: Data Cube  OLAP all Europe North_America Mexico Canada Spain Germany Vancouver M. Wind L. Chan ... ... ... ... ... ... all region office country Toronto Frankfurt city
  • 10. Data Warehouses: Data Cube  OLAP Product Region Month Dimensions: Product, Location, Time Hierarchical summarization paths Industry Region Year Category Country Quarter Product City Month Week Office Day
  • 11. Data Warehouses: Data Cube  OLAP Total annual sales of TV in U.S.A. Date Product Country All, All, All sum sum TV VCR PC 1Qtr 2Qtr 3Qtr 4Qtr U.S.A Canada Mexico sum
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.
  • 17.
  • 18.
  • 19.
  • 20.
  • 21.
  • 22.
  • 23.
  • 24.
  • 25.
  • 26.
  • 27.
  • 28.
  • 29.
  • 30.
  • 31.
  • 32. Classification Process (1): Model Construction Training Data Classification Algorithms IF rank = ‘professor’ OR years > 6 THEN tenured = ‘yes’ Classifier (Model)
  • 33. Classification Process (2): Use the Model in Prediction Classifier Testing Data Unseen Data (Jeff, Professor, 4) Tenured?
  • 34.
  • 35. Q and A Thank you !!!