1. MG-CHARM
ANURAG SAXENA
by
Minimal generator close hierarchical association rule mining

2. Contents
 Abstract
 Introduction
 Scope
 Existing System
 Proposed System
 Algorithm
 Example
 Design
 Functional Diagram
 Structural Diagram
 Screens
 Conclusion
 References

3. Abstract
 In Data Mining, Frequent Closed Itemsets (FCIs) are usually fewer than frequent
itemsets.
 However, it is necessary to find Minimal Generators (mGs) for association rule from
them.
 The finding mGs approaches based on generating candidate loose timelines when
the number of FCIs are large.
 To overcome the loose of timelines we implemented an algorithm called MG-
CHARM.

4. Introduction
 An efficient and fast algorithm for finding mGs of FCIs.
 Time of MG-CHARM is fewer than the time of finding mGs after finding all closed
itemsets (CHARM), especially in case of the length of each FCI is long.
 At present, almost all algorithms for mining mGs of FCIs are based on Apriori
algorithm.
 Applying for Market Basket Analysis.

5. Scope
Although Market Basket Analysis conjures up pictures of shopping carts, and
supermarket shoppers, it is important to realize that there are many other areas in which
it can be applied.
These include:
 Analysis of credit card purchases.
 Analysis of telephone calling patterns.
 Identification of fraudulent medical insurance claims.
 Analysis of telecom service purchases.

6. Existing System
 In first method, found candidates that are mGs first, then defined their closures to
find out FCIs.
 In second method, found all FIs using CHARM algorithm.
 Then used level-wise method to find out all mGs that correspond to each closed
itemset.

7. Proposed System
 Both of the previous methods have the disadvantage in large size of frequent
itemsets since the number of considered candidates is large.
 The proposed method is a fast algorithm for mining mGs based on CHARM.
 It overcomes the disadvantage of above two methods by using CHARM to generate
FCI and also find mGs of them.
 This proposed algorithm is named as MG-CHARM.

8. Algorithm
Input: The database D and support
threshold minS
Output: all FCI satisfy minSup and their
Method:
MG-CHARM(D, minSup)
[0]={IjXl(li) ,(li): t,E 1/ cr(/ i) :2: minSup}
MG-CIIARM-EXTEND([0], C= 0 )
Return C
MG-CHARM-EXTEND([P], C)
for each lixI(lj),mG(li) in [PJ do
Pi= Pj U Ii and [PiJ = 0
for each IjxI(lj),mG(Ij) in [PJ,with j > i do
X = Ij and Y=I(li) n 1(1j)
MG-CHARM-PROPERTY(XxY,/i,lj ,Pi,
[Pi],[P])
SUBSUMPTION-CHECK(C, Pi)
MG-CIIARM-EXTEND([Pi],C)
MG-CHARM-PROPERTY(XxY,li,lj,Pi,
[Pi],[P])
if cr(X):2: minSup then
if I(Ii) = I@ then II property I
Remove Ij from P
Pi=Pi UIj
mG(Pi) = mG(Pi) +mG(lj)
else if I(li)C I@ then II property 2
Pi =Pj ulj
else if l(lj) :::J I(lj) then II property 3
Remove Ij from [P]
Add Xx Y,mG(Ij) to [Pi]
else if I(li) *-1(1j) then II property 4
AddXxY, u [mG(li), mG@]to [Pi]

9. Example
1. Transaction Database
Consider the database
Transaction
ID
Content
1 A, C, T, W
2 C, D, W
3 A, C, T, W
4 A, C, D, W
5 A, C, D, T, W
6 C, D, T
Example database
Item Transactions
A 1, 3, 4, 5
C 1, 2, 3, 4, 5, 6
D 2, 4, 5, 6
T 1, 3, 5, 6
W 1, 2, 3, 4, 5
Vertical format

10. 2. Support
 The number of transactions that has the given item set.
Support of ACW=4
3. Frequent Itemset
 An item set is said to be frequent item set if it is greater than or equal to the
minSup.
 minSup is the value given by the user.
If minSup = 4 then ACW is a frequent item set.

11. 4. Galois Connection
t(ACW) = t(A)  t(C)  t(W) i(245) = i(2)  i(4)  i(5)
= 1345  123456  12345 = CDW  ACDW ACDTW
= 1345 = CDW
X t(X)
i(Y) Yi
t

12. 5. Closure Operator
 If c(X) = i(t(X)) then c(X) is called closure operator.
c(AW) = i(t(AW))
= i(1345)
= ACW
6. Frequent Closed Itemset
 An item set X is said to be frrequent closed itemset if X is
- Frequent Itemset and
- Closure

13. Illustrations
Consider minSup = 50%
Since number of transactions = 6
minSup = 3
{}123456
A1345T1356 C123456D2456 W12345
W CATD

14. Illustration of updating mG
{}123456
A1345T1356 C123456D2456 W12345
W CATDC
DT56
DT
DWC245
DW
DA45
DA

15. Functional Diagram
Use Case Diagram

16. Sequence Diagram

17. Structural Diagram

18. Screens
Logon Screen

19. Recruit Employee Screen for Admin

20. Frequent Items

21. Frequent Items

22. Conclusion
 This is a new method for mining mGs of FCIs need not generate candidates.
Experiments showed that the time of updating mGs of frequent closed
itemsets is insignifant. Especially in case of the large case of frequent
itemsets, the time of updating is very fewer than the methods implementd
before.
 In future, we can apply this achievement to the problems of mining non-
redundant association rules, non-redundant rules query.

23. References
[1]. IEEE supporting paper on the title “Fast Algorithm for Mining Minimal Generators
of Frequent Closed Itemsets and Their Applications”.
Link: http://ieeexplore.ieee.org/
[2]. Previous knowledge about mining frequent patterns, associations, and
correlations from the text book ‘Data Mining Concepts and techniques by
Micheline Kamber 2nd Edition’.
[3]. Information about Market basket Analysis.
Link: http://www.information-drivers.com/market_basket_analysis.php