1. Progressive Weighted Miner: An Efficient Method for Time-Constraint Mining
Chang-Hung Lee, Jian Chih Ou, and Ming Syan Chen, Proc. of the 7th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD’03)
Adviser: Jia-Ling Koh
Speaker: Yu-ting Kung

2. Introduction
Introduce a weighted model of transaction-weighted association rules in a time-variant database.
Propose an efficient Progressive Weighted Miner (PWM) algorithm to produce weighted association rules

3. Introduction (Cont.) Progressive Weighted Miner
The importance of each transaction period is first reflected by a proper weight assigned by the user.
PWM partitions the time-variant database in light of weighted periods of transactions and performs weighted mining.

4. Problem Description Weighted minimum support Weighted-Support of X
Weighted support ratio of an itemset X
Amount of partial transactions
Corresponding weight values by “weighting function”
The number of transactions in partition Pi that contain itemset X

5. Problem Description (Cont.)
Weighted-Confidence
Frequent weighted association rule (X=>Y)W
, and

6. Problem Description (Cont.)
For example (min_sup=30%, min_conf=75%, W(P1) = 0.5, W(P2) = 1 and W(P3) = 2)
Min_SW={4X0.5+4X1+4X2}X0.3=4.2
(C=>B)Wis frequent?
Transaction Database
Date
TID
Itemset P1
Jan-02 t1 B D t2 A t3 C t4 P2
Feb-02 t5 E t6 t7 t8 P3
Mar-02 t9
t10 F
t11
t12
Yes!!
> min_sup
> min_conf

7. Partition database based
PWM Algorithm
Procedure I
Partition database based
on weighted periods
1st Scan database
Procedure II
W(Pi)
Produce C2
Procedure III
W(Pi)
Use C2 to
produce Ck
2st Scan database
Procedure IV
Generate LK
(X=>Y)W

8. Procedure I Time granularity= month, W(P1) = 0.5, W(P2) = 1, W(P3) = 2
Transaction Database
Date
TID
Itemset
Jan-02 t1 B D t2 A t3 C t4
Feb-02 t5 E t6 t7 t8
Mar-02 t9
t10 F
t11
t12 P1 P2 P3
Time granularity= month,
W(P1) = 0.5, W(P2) = 1, W(P3) = 2

9. Procedure II p1 p2 scan scan 4x0.5x0.3
Min_SW(P1+P2)=1.8, Min_SW(P2)=1.2 C2
start
NW(X)count AB 2
1*1=1 AC BC 1
1+2*1=3 BD
1+0*1=1 BE CD CE
2*1=2 DE
Min_SW(P1)=0.6 C2
start
NW(X)count BD 1
2*0.5=1 AD
1*0.5=0.5 BC CD

10. Procedure II (Cont.) scan p3
Min_SW(P1+P2+P3)=4.2, Min_SW(P2+P3)=3.6, Min_SW(P3)=2.4 C2
start
NW(X)count AD 3
1*2=2 BC 1
3+1*2=5 BD BE BF
3*2=6 CE 2
2+1*2=4 CF DE
2+0*2=2 DF EF p3
scan

11. Procedure III After 1st scan D, candidate itemsets:
{B}, {C}, {E}, {F}, {BC}, {BF}, {CE}
(because C2 = {BC,CE,BF} from Procedure II)

12. Procedure IV After 2nd scan D pruning Min_SW(D)=4.2 Candidate
NW(X)count C1
{B}
3*0.5+2*1+3*2=9.5
{C}
2*0.5+3*1+1*2=6
{E}
3*1+1*2=5
{F}
3*2=6 C2
{BC}
2*0.5+2*1+1*2=5
{BF}
{CE}
2*1+1*2=4
Frequent Itemsets
NW(X)count L1
{B}
9.5
{C} 6
{E} 5
{F} L2
{BC}
{BF}
pruning

13. WAR pruning Rules Support Confidence B => C
5/(4*0.5+4*1+4*0.2)= 35.7%
5/9.5=52.6%
B => F
6/(4*0.5+4*1+4*0.2)= 42.8%
6/9.5=63.1%
C => B
5/6=83.3%
F => B
6/6=100%
pruning
Rules
Support
Confidence
C => B
35.7%
83.3%
F => B
42.8%
100%

14. Conclusion Develop PWM to generate the WAR in Time-variant database
PWM employs a filtering threshold in each partition
PWM can lead to more interesting results