The document discusses decision trees and provides two sample datasets - one on weather and playing outcomes and another on attributes like age, income, student status, credit rating, and likelihood of buying a computer. It introduces an algorithm for building decision trees and notes the second dataset comes from Quinlan's ID3 example for demonstrating decision trees.

1. Data Warehouse and Data Mining Chapter 61
Decision TreesDecision Trees
An Algorithm for Building Decision Trees

2. Data Warehouse and Data Mining 2
Weather Data with ID code
ID Outlook Temperature Humidity Windy Play?
A sunny hot high false No
B sunny hot high true No
C overcast hot high false Yes
D rain mild high false Yes
E rain cool normal false Yes
F rain cool normal true No
G overcast cool normal true Yes
H sunny mild high false No
I sunny cool normal false Yes
J rain mild normal false Yes
K sunny mild normal true Yes
L overcast mild high true Yes
M overcast hot normal false Yes
N rain mild high true No

3. Data Warehouse and Data Mining Chapter 63
Training Dataset
age income student credit_rating buys_computer
<=30 high no fair no
<=30 high no excellent no
30…40 high no fair yes
>40 medium no fair yes
>40 low yes fair yes
>40 low yes excellent no
31…40 low yes excellent yes
<=30 medium no fair no
<=30 low yes fair yes
>40 medium yes fair yes
<=30 medium yes excellent yes
31…40 medium no excellent yes
31…40 high yes fair yes
>40 medium no excellent no
This follows an example from Quinlan’s ID3