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Figure 6-10. Filtered results for only cluster 0 observations.
Sonia could use these figures to begin contacting potential participants in her programs. With the
high risk group having weights between 167 and 203 pounds, and cholesterol levels between 204
and 235 (these are taken from the Range statistics in Figure 6-10), she could return to her
company's database and issue a SQL query like this one:
SELECT First_Name, Last_Name, Policy_Num, Address, Phone_Num
FROM PolicyHolders_view
WHERE Weight >= 167
AND Cholesterol >= 204;
This would give her the contact list for every person, male or female, insured by her employer who
would fall into the higher risk group (cluster 0) in our data mining model. She could change the
parameter criteria in our Filter Examples operator to be cluster=cluster_1 and re-run the model to
get the descriptive statistics for those in the next highest risk group, and modify her SQL statement
to get the contact list for that group from her organizational database; something akin to this
query:
SELECT First_Name, Last_Name, Policy_Num, Address, Phone_Num
FROM PolicyHolders_view
WHERE (Weight >= 140 AND Weight <= 169)
AND (Cholesterol >= 168 AND Cholesterol <= 204);
If she wishes to also separate her groups by gender, she could add that criteria as well, such as
“AND Gender = 1” in the WHERE clause of the SQL statement. As she continues to develop
her health improvement programs, Sonia would have the lists of individuals that she most wants to
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