1. Cluster Analysis
Using RapidMiner and SAS 9.3

2. Agenda
• The Data
• Some preliminary treatments
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Checking for outliers
Manual outlier checking for a given confidence level
Filtering outliers
Data without outliers
Selecting attributes for clusters
Setting up clusters
Reading the clusters
Using SAS for clustering
Dendrogram
Depicting Tree using SAS
Conclusion

3. The Data
• Number of observations: 97
• 3 numeric variables:
 Birth rate per thousand
 Death rate per thousand
 Infant mortality rate per thousand
• 1 polynomial variable: Country
• Data obtained from UN Demographic
Yearbook 1990

4. Some preliminary treatments
• Checking for outliers using RapidMiner

5. Some preliminary treatments
• Manual checking for outliers at a given confidence
level
• For Birth (95%)
 mu-2(sigma) = 27.384-2(12.978) = 1.428
 mu+2(sigma) = 27.384+2(12.978) = 53.34
• Hence, no outliers

6. • Filtering outliers
o 10 outliers recorded

7. • Data without outliers
o Filter examples
o Parameter string: outlier=true
o Invert filter

8. • Selecting attributes for clusters
o Clusters on polynomial variables make no sense
o Remove Country from attribute list

9. • Setting up clusters
o K=3
o Join both nodes to get cluster model information

10. Reading the Clusters
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Cluster 1: Low values of each numeric variable
Cluster 2: High values of each numeric variable
Cluster 0: Moderate values of each numeric variable

11. Reading the Clusters
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Scatter Plot Birth and Death against Infant Death
Rate
Size – Infant Death Rate

12. Using SAS for clustering
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Using canonical variables for standardization of
variables to mean 0 and standard deviation 1
Spherical within-cluster covariance matrix
proc aceclus data=Poverty out=Ace p=.03
noprint;
var Birth Death InfantDeath;
run;
proc cluster data=Ace outtree=Tree
method=ward
ccc pseudo print=15;
var can1 can2 can3 ;
id Country;
run;

13. Using SAS for clustering
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First 2 canonical variables account for about 93% of
the total variation

14. Dendrogram

15. Tree depiction
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Plot can1 and can2 against cluster
Shows similar plot compared to RapidMiner output

16. Conclusion
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Cluster 1: Mostly developed European nations, USA, UK,
Singapore, USSR, etc
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Cluster 2: Afghanistan, Pakistan, Iran, mostly under
privileged African nations
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Efficient allocation of public goods
Lower crime rates
Abortion legalized
Low GDP
Abortion not legal
High crime rates, prevalent wars and terrorist activities
Poor health standards, high poverty levels
Cluster 0: India, Mexico, South Africa, Saudi Arabia, etc
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Emerging nations
Increasing growth rates
Controlled negative externalities
Focus on literacy and employment