5. Introduction
• Facing the age of data
explosion, the amount of
data is increasing very fast
in databases.
• Those data normally include
hidden knowledge, and they
can be used to improve the
decision-making process of
any kinds of company.
Wu Bo-Han rippleblue2002@gmail.com
6. Classification rule
• Classification rule mining is a common
technology in data mining.
• From the historical data, rule can be generalized
to classify unknown samples or predict the future.
Wu Bo-Han rippleblue2002@gmail.com
7. Classification rule
• IF <some conditions are satisfied> AND <some
conditions are satisfied> THEN <assign some
values of the goal attribute>
• Example:
IF Sex=Male AND Location = Taipei THEN
Product= beer
Wu Bo-Han rippleblue2002@gmail.com
8. Classification rule
• Traditional mining techniques mostly focus on
accuracy and usually generate lots of rules that
are hard to choose meaningful ones from.
• In order to select optimally meaningful rules,
accuracy, comprehensibility and interestingness
are employed as three objectives.
Wu Bo-Han rippleblue2002@gmail.com
9. Accuracy
sup( A & C )
A(R)
sup( A )
•
•
is the support for the rule R
represents the support for the antecedent
of rule R
Wu Bo-Han rippleblue2002@gmail.com
10. Comprehensibility
Nc ( R)
C( R) 1
Mc
• Nc(R)is the number of conditions in the rule
• Mc is the maximum number of conditions that a
rule can have
Wu Bo-Han rippleblue2002@gmail.com
11. Interestingness
sup( A & C ) sup( A & C ) sup( A & C )
I (R)
1
sup( A )
sup( C )
D
• 1
• 1
•
gives the probability of generating the rule depending on the antecedent part
gives the probability of generating the rule depending on the consequent part
gives the probability of generating the rule depending on the whole data-set
Wu Bo-Han rippleblue2002@gmail.com
12. Multi-objective optimization
Low price and high performance
90%
Performance
40%
10k
Non‐dominated solution
Price
100k
Wu Bo-Han rippleblue2002@gmail.com
14. Multi-objective optimization
Low price and high performance
90%
4
5
3
2
Performance
40%
Non‐dominated solution set
Non‐dominated solution
1
10k
Price
100k
Wu Bo-Han rippleblue2002@gmail.com
15. Multi-objective optimization
• However, traditional methods handle multiobjective problems by converting them into a
single objective problem.
• But this approach can not guarantee to find
optimal solutions for multiple objectives.
Wu Bo-Han rippleblue2002@gmail.com
16. SPEA2
• SPEA2 is designed by the
concept "survival of the fittest"
from natural evolution.
• The work intended to improve
quality of individuals from
solution space in each
generation.
• SPEA2 used the strategy of
selection, crossover and
mutation to retain the best
individuals and discard worst
ones.
Wu Bo-Han rippleblue2002@gmail.com
33. Non-dominated rules
• Three objectives
IF Sex=Male AND Location = Taipei
THEN Product= beer
A = 0.333333
C = 0.875000
I = 0.080000
– Accuracy
– Comprehensibility
– Interestingness
Non‐dominated rules
Wu Bo-Han rippleblue2002@gmail.com
37. Case study
Non-dominated rules
Sales methods=臨櫃保險 AND Data source=百貨公司 AND Company=外商壽險公司
THEN Product=短年期壽險
Payment methods=現金 AND Data source=百貨公司 AND Company=外商壽險公司
THEN Product=短年期壽險
Payment frequency=月 AND Data source=百貨公司 Company=外商壽險公司
Wu Bo-Han rippleblue2002@gmail.com
38. Case study
Non-dominated rules
Sales methods=臨櫃保險 AND Data source=
百貨公司 AND Company=外商壽險公司
THEN Product=短年期壽險
「透過臨櫃保險參加保險的百貨公司
客戶,較會考慮在外商壽險公司購買
短年期壽險」
表示外商壽險公司在針對以臨櫃購買
保險的百貨公司客戶,可以推薦短年
期壽險。
Wu Bo-Han rippleblue2002@gmail.com
39. Thanks for your listening
Wu Bo-Han rippleblue2002@gmail.com