Education for all..

1. DWH-Ahsan AbdullahDWH-Ahsan Abdullah
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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

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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

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What is Data Mining?: Slightly formalWhat is Data Mining?: Slightly formal

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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).

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Why Data Mining? Huge volumeWhy Data Mining? Huge volume

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Claude Shannon's info. theoryClaude Shannon's info. theory
More volume means
less information

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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

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Why Data Mining?: Supply & DemandWhy Data Mining?: Supply & Demand

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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

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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)

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Data Mining Vs StatisticsData Mining Vs Statistics

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Data Mining Vs. StatisticsData Mining Vs. Statistics

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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
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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

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Data Mining is…Data Mining is…
 Decision TreesDecision Trees
 Neural NetworksNeural Networks
 Rule InductionRule Induction
 ClusteringClustering
 Genetic AlgorithmsGenetic Algorithms
If. . . . .If. . . . .
Then. . .Then. . .

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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


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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: