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Introduction to data warehouse

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Cognizant Technology Solutions
Cognizant Technology Solutions
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Data Mining: Concepts and Techniques — Chapter 1 — — Introduction —
Syllabus ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Chapter 1. Introduction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Why Data Mining? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Evolution of Sciences ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Evolution of Database Technology ,[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]
What Is Data Mining? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Knowledge Discovery (KDD) Process ,[object Object],Data Cleaning Data Integration Databases Data Warehouse Knowledge Task-relevant Data Selection Data Mining Pattern Evaluation
Data Mining and Business Intelligence Increasing potential to support business decisions End User Business Analyst Data Analyst DBA Decision Making Data Presentation Visualization Techniques Data Mining Information Discovery Data Exploration Statistical Summary, Querying, and Reporting Data Preprocessing/Integration, Data Warehouses Data Sources Paper, Files, Web documents, Scientific experiments, Database Systems
Data Mining: Confluence of Multiple Disciplines Data Mining Database Technology Statistics Machine Learning Pattern Recognition Algorithm Other Disciplines Visualization
Why Not Traditional Data Analysis? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Multi-Dimensional View of Data Mining ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Mining: Classification Schemes ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Mining: On What Kinds of Data? ,[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 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
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]
Major Issues in Data Mining ,[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]
Summary ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
 
Supplementary Lecture Slides ,[object Object],[object Object],[object Object]
Why Data Mining?—Potential Applications ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Ex. 1: Market Analysis and Management ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Ex. 2: Corporate Analysis & Risk Management ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Ex. 3: Fraud Detection & Mining Unusual Patterns ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
KDD Process: Several Key Steps ,[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]
Are All the “Discovered” Patterns Interesting? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Find All and Only Interesting Patterns? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Other Pattern Mining Issues ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
A Few Announcements (Sept. 1) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Why Data Mining Query Language? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Primitives that Define a Data Mining Task ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Primitive 3: Background Knowledge ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Primitive 4: Pattern Interestingness Measure ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Primitive 5: Presentation of Discovered Patterns ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
DMQL—A Data Mining Query Language ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
An Example Query in DMQL
Other Data Mining Languages & Standardization Efforts ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Integration of Data Mining and Data Warehousing ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Coupling Data Mining with DB/DW Systems ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Architecture: Typical Data Mining System data cleaning, integration, and selection Database or Data Warehouse Server Data Mining Engine Pattern Evaluation Graphical User Interface Knowledge-Base Database Data Warehouse World-Wide Web Other Info Repositories

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Introduction to data warehouse

  • 1. Data Mining: Concepts and Techniques — Chapter 1 — — Introduction —
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9. Data Mining and Business Intelligence Increasing potential to support business decisions End User Business Analyst Data Analyst DBA Decision Making Data Presentation Visualization Techniques Data Mining Information Discovery Data Exploration Statistical Summary, Querying, and Reporting Data Preprocessing/Integration, Data Warehouses Data Sources Paper, Files, Web documents, Scientific experiments, Database Systems
  • 10. Data Mining: Confluence of Multiple Disciplines Data Mining Database Technology Statistics Machine Learning Pattern Recognition Algorithm Other Disciplines Visualization
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.
  • 17.
  • 18.
  • 19.  
  • 20.
  • 21.
  • 22.
  • 23.
  • 24.
  • 25.
  • 26.
  • 27.
  • 28.
  • 29.
  • 30.
  • 31.
  • 32.
  • 33.
  • 34.
  • 35.
  • 36. An Example Query in DMQL
  • 37.
  • 38.
  • 39.
  • 40. Architecture: Typical Data Mining System data cleaning, integration, and selection Database or Data Warehouse Server Data Mining Engine Pattern Evaluation Graphical User Interface Knowledge-Base Database Data Warehouse World-Wide Web Other Info Repositories