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DWH-Ahsan AbdullahDWH-Ahsan Abdullah
11
Data WarehousingData Warehousing
Lecture-29Lecture-29
Brief Intro. to Data MiningBrief Intro. to Data Mining
Virtual University of PakistanVirtual University of Pakistan
Ahsan Abdullah
Assoc. Prof. & Head
Center for Agro-Informatics Research
www.nu.edu.pk/cairindex.asp
National University of Computers & Emerging Sciences, Islamabad
Email: ahsan101@yahoo.com
DWH-Ahsan Abdullah
2
What is Data Mining?: Non technical viewWhat is Data Mining?: Non technical view
““There are things that we know that weThere are things that we know that we
know…know…
there are things that we know that wethere are things that we know that we
don’t know…don’t know…
there are things that wethere are things that we don’t knowdon’t know wewe
don’t knowdon’t know.”.”
Donald RumsfieldDonald Rumsfield
US Secretary of DefenceUS Secretary of Defence
DWH-Ahsan Abdullah
3
What is Data Mining?: Slightly formalWhat is Data Mining?: Slightly formal
DWH-Ahsan Abdullah
4
What is Data Mining?: Formal viewWhat is Data Mining?: Formal view
Data mining digs outData mining digs out valuablevaluable non-trivialnon-trivial
informationinformation fromfrom largelarge multidimensionalmultidimensional
apparentlyapparently unrelatedunrelated data bases(sets).data bases(sets).
DWH-Ahsan Abdullah
5
Why Data Mining? Huge volumeWhy Data Mining? Huge volume
DWH-Ahsan Abdullah
6
Claude Shannon's info. theoryClaude Shannon's info. theory
More volume means
less information
DWH-Ahsan Abdullah
7
Volume of Data
Value vs. VolumeValue vs. Volume
Value
of
Data
Decision (Y/N)Decision (Y/N)
Decision SupportDecision Support
KnowledgeKnowledge
InformationInformation
Indexed DataIndexed Data
Raw DataRaw Data
DWH-Ahsan Abdullah
8
Why Data Mining?: Supply & DemandWhy Data Mining?: Supply & Demand
DWH-Ahsan Abdullah
9
DWH-Ahsan Abdullah
10
Data Mining isData Mining is HOT!HOT!
 10 Hottest Jobs of year 202510 Hottest Jobs of year 2025
Time MagazineTime Magazine, 22 May, 2000, 22 May, 2000
 10 emerging areas of technology10 emerging areas of technology
MIT’s Magazine of Technology ReviewMIT’s Magazine of Technology Review,,
Jan/Feb, 2001Jan/Feb, 2001
DWH-Ahsan Abdullah
11
How Data Mining is different? TraditionallyHow Data Mining is different? Traditionally
 Data WarehousesData Warehouses ((Data-driven explorationData-driven exploration))::
 Data Mining (Knowledge-driven explorationKnowledge-driven exploration)
 Traditional Database (Transactions):
 Knowledge Discovery (KDD)
DWH-Ahsan Abdullah
12
Data Mining Vs StatisticsData Mining Vs Statistics
DWH-Ahsan Abdullah
13
Data Mining Vs. StatisticsData Mining Vs. Statistics
DWH-Ahsan Abdullah
14
Knowledge extraction using statisticsKnowledge extraction using statistics
Inflation Vs Stock inedx increase
0
10
20
30
40
1.6 1.7 1.8 1.85 1.9 1.95 2 2.9 3 3.3 4.2 4.4 5 6
Inflation (%)
Stockincrease
(%)
Q: What will be the stock increase when inflation is 6%?
A: Model non-linear relationship using a line y = mx + c.
Hence answer is 13%
DWH-Ahsan AbdullahDWH-Ahsan Abdullah
1515
0
10000
20000
30000
40000
50000
60000
70000
0 5 10 15 20 25 30 35
y = -0.0127x6
+ 1.5029x5
- 63.627x4
+ 1190.3x3
- 9725.3x2
+ 31897x - 29263
-10000
0
10000
20000
30000
40000
50000
60000
70000
0 5 10 15 20 25 30 35
Failure of regression modelsFailure of regression models
DWH-Ahsan Abdullah
16
Data Mining is…Data Mining is…
 Decision TreesDecision Trees
 Neural NetworksNeural Networks
 Rule InductionRule Induction
 ClusteringClustering
 Genetic AlgorithmsGenetic Algorithms
If. . . . .If. . . . .
Then. . .Then. . .
DWH-Ahsan Abdullah
17
Data Mining is NOT ...Data Mining is NOT ...
 Data warehousingData warehousing
 Ad Hoc Query / ReportingAd Hoc Query / Reporting
 Online Analytical Processing (OLAP)Online Analytical Processing (OLAP)
 Data VisualizationData Visualization
 Software AgentsSoftware Agents

DWH-Ahsan Abdullah
18
Data Mining:Data Mining: BusinessBusiness PerspectivePerspective
 ““knowledge” is worth knowing if it can be used toknowledge” is worth knowing if it can be used to
increase profit by lowering cost or it can be used toincrease profit by lowering cost or it can be used to
increase profit by raising revenue.increase profit by raising revenue.
Business questionsBusiness questions
 Profiling/Segmentation
 Cross-Service
 Employee retention:

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Lecture 29

  • 1. DWH-Ahsan AbdullahDWH-Ahsan Abdullah 11 Data WarehousingData Warehousing Lecture-29Lecture-29 Brief Intro. to Data MiningBrief Intro. to Data Mining Virtual University of PakistanVirtual University of Pakistan Ahsan Abdullah Assoc. Prof. & Head Center for Agro-Informatics Research www.nu.edu.pk/cairindex.asp National University of Computers & Emerging Sciences, Islamabad Email: ahsan101@yahoo.com
  • 2. DWH-Ahsan Abdullah 2 What is Data Mining?: Non technical viewWhat is Data Mining?: Non technical view ““There are things that we know that weThere are things that we know that we know…know… there are things that we know that wethere are things that we know that we don’t know…don’t know… there are things that wethere are things that we don’t knowdon’t know wewe don’t knowdon’t know.”.” Donald RumsfieldDonald Rumsfield US Secretary of DefenceUS Secretary of Defence
  • 3. DWH-Ahsan Abdullah 3 What is Data Mining?: Slightly formalWhat is Data Mining?: Slightly formal
  • 4. DWH-Ahsan Abdullah 4 What is Data Mining?: Formal viewWhat is Data Mining?: Formal view Data mining digs outData mining digs out valuablevaluable non-trivialnon-trivial informationinformation fromfrom largelarge multidimensionalmultidimensional apparentlyapparently unrelatedunrelated data bases(sets).data bases(sets).
  • 5. DWH-Ahsan Abdullah 5 Why Data Mining? Huge volumeWhy Data Mining? Huge volume
  • 6. DWH-Ahsan Abdullah 6 Claude Shannon's info. theoryClaude Shannon's info. theory More volume means less information
  • 7. DWH-Ahsan Abdullah 7 Volume of Data Value vs. VolumeValue vs. Volume Value of Data Decision (Y/N)Decision (Y/N) Decision SupportDecision Support KnowledgeKnowledge InformationInformation Indexed DataIndexed Data Raw DataRaw Data
  • 8. DWH-Ahsan Abdullah 8 Why Data Mining?: Supply & DemandWhy Data Mining?: Supply & Demand
  • 10. DWH-Ahsan Abdullah 10 Data Mining isData Mining is HOT!HOT!  10 Hottest Jobs of year 202510 Hottest Jobs of year 2025 Time MagazineTime Magazine, 22 May, 2000, 22 May, 2000  10 emerging areas of technology10 emerging areas of technology MIT’s Magazine of Technology ReviewMIT’s Magazine of Technology Review,, Jan/Feb, 2001Jan/Feb, 2001
  • 11. DWH-Ahsan Abdullah 11 How Data Mining is different? TraditionallyHow Data Mining is different? Traditionally  Data WarehousesData Warehouses ((Data-driven explorationData-driven exploration))::  Data Mining (Knowledge-driven explorationKnowledge-driven exploration)  Traditional Database (Transactions):  Knowledge Discovery (KDD)
  • 12. DWH-Ahsan Abdullah 12 Data Mining Vs StatisticsData Mining Vs Statistics
  • 13. DWH-Ahsan Abdullah 13 Data Mining Vs. StatisticsData Mining Vs. Statistics
  • 14. DWH-Ahsan Abdullah 14 Knowledge extraction using statisticsKnowledge extraction using statistics Inflation Vs Stock inedx increase 0 10 20 30 40 1.6 1.7 1.8 1.85 1.9 1.95 2 2.9 3 3.3 4.2 4.4 5 6 Inflation (%) Stockincrease (%) Q: What will be the stock increase when inflation is 6%? A: Model non-linear relationship using a line y = mx + c. Hence answer is 13%
  • 15. DWH-Ahsan AbdullahDWH-Ahsan Abdullah 1515 0 10000 20000 30000 40000 50000 60000 70000 0 5 10 15 20 25 30 35 y = -0.0127x6 + 1.5029x5 - 63.627x4 + 1190.3x3 - 9725.3x2 + 31897x - 29263 -10000 0 10000 20000 30000 40000 50000 60000 70000 0 5 10 15 20 25 30 35 Failure of regression modelsFailure of regression models
  • 16. DWH-Ahsan Abdullah 16 Data Mining is…Data Mining is…  Decision TreesDecision Trees  Neural NetworksNeural Networks  Rule InductionRule Induction  ClusteringClustering  Genetic AlgorithmsGenetic Algorithms If. . . . .If. . . . . Then. . .Then. . .
  • 17. DWH-Ahsan Abdullah 17 Data Mining is NOT ...Data Mining is NOT ...  Data warehousingData warehousing  Ad Hoc Query / ReportingAd Hoc Query / Reporting  Online Analytical Processing (OLAP)Online Analytical Processing (OLAP)  Data VisualizationData Visualization  Software AgentsSoftware Agents 
  • 18. DWH-Ahsan Abdullah 18 Data Mining:Data Mining: BusinessBusiness PerspectivePerspective  ““knowledge” is worth knowing if it can be used toknowledge” is worth knowing if it can be used to increase profit by lowering cost or it can be used toincrease profit by lowering cost or it can be used to increase profit by raising revenue.increase profit by raising revenue. Business questionsBusiness questions  Profiling/Segmentation  Cross-Service  Employee retention:

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