Transcript Database Clustering and Summary Generation

Example: Decision Tree Approach
Christoph F. Eick: Introduction Knowledge Discovery and Data Mining (KDD)
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Decision Tree Approach2
Christoph F. Eick: Introduction Knowledge Discovery and Data Mining (KDD)
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Decision Trees
Example:
• Conducted survey to see what customers were
interested in new model car
• Want to select customers for advertising campaign
sale
custId
c1
c2
c3
c4
c5
c6
car
taurus
van
van
taurus
merc
taurus
age
27
35
40
22
50
25
city newCar
sf
yes
la
yes
sf
yes
sf
yes
la
no
la
no
Christoph F. Eick: Introduction Knowledge Discovery and Data Mining (KDD)
training
set
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One Possibility
sale
custId
c1
c2
c3
c4
c5
c6
age<30
Y
N
city=sf
Y
likely
car
taurus
van
van
taurus
merc
taurus
age
27
35
40
22
50
25
city newCar
sf
yes
la
yes
sf
yes
sf
yes
la
no
la
no
car=van
N
unlikely
Y
likely
N
unlikely
Christoph F. Eick: Introduction Knowledge Discovery and Data Mining (KDD)
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Another Possibility
sale
custId
c1
c2
c3
c4
c5
c6
car=taurus
Y
N
city=sf
Y
likely
car
taurus
van
van
taurus
merc
taurus
age
27
35
40
22
50
25
city newCar
sf
yes
la
yes
sf
yes
sf
yes
la
no
la
no
age<45
N
unlikely
Y
likely
N
unlikely
Christoph F. Eick: Introduction Knowledge Discovery and Data Mining (KDD)
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Classification Data Sets

http://www.ics.uci.edu/~mlearn/MLRepository.html
– http://www.ics.uci.edu/~mlearn/MLSummary.html
Christoph F. Eick: Introduction Knowledge Discovery and Data Mining (KDD)
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