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Contents
Abstract
Introduction
Scope
Existing System
Proposed System
Algorithm
Example
Design
Functional Diagram
Structural Diagram
Screens
Conclusion
References
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.
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.
ScopeAlthough 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.
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.
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.
AlgorithmInput: 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 CMG-CHARM-EXTEND([P], C)for each lixI(lj),mG(li) in [PJ doPi= Pj U Ii and [PiJ = 0for each IjxI(lj),mG(Ij) in [PJ,with j
> i doX = 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 thenif I(Ii) = I@ then II property IRemove Ij from PPi=Pi UIjmG(Pi) = mG(Pi) +mG(lj)else if I(li)C I@ then II property 2Pi =Pj uljelse if l(lj) :::J I(lj) then II property
3Remove Ij from [P]Add Xx Y,mG(Ij) to [Pi]else if I(li) *-1(1j) then II property 4AddXxY, u [mG(li), mG@]to [Pi]
Example1. 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, TExample 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
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.
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
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
Illustrations
Consider minSup = 50%
Since number of transactions = 6
minSup = 3
{}123456
A1345T1356C1234
56D2456
W12345
W CATD
Illustration of updating mG {}1234
56
A1345T1356C1234
56D2456
W12345
W CATDC
DT56
DTDWC24
5
DW
DA45
DA
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.
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