Association rule mining

1. Presentation On
Association Rule Mining
Under the Guidance of :
Prof: R.N.Yadwad
Presented By :
Poornima Raidurg
2SD10CS057

2. Data Mining
• Data Mining or knowledge discovery, is the
computer-assisted process of digging through and
analyzing enormous sets of data and then extracting
the meaning of the data.
Example :
• Market Basket Analysis - Understand what products or
services are commonly purchased together

3. Association Analysis
• It is the most important model invented and extensively
studied by databases and data mining community.
• Proposed by Agrawal Rakesh, Srikrishna.
• Association rules are used to discover patterns that describe
strongly associated features in the data.
• Application - Business field where discovering of purchase
patterns or association between products is very useful for
decision-making and effective marketing.

4. Association Rules
• Association rules are of the form X->Y where X and Y are
disjoint item sets.
• The strength of an association rule can be determined
in terms of Support and Confidence.

5. Notations
 Item set is a collection of zero or more
items.
 If an item set contains ‘k’ items then it is
k-item set.

6. Procedure
Two subtasks:
Step 1 - Frequent Itemset Generation :It finds all the
itemsets that satisfy user-defined minsup threshold.
Step 2 - Rule Generation : It extracts all the high
confidence rules from the frequent itemsets found in
Step 1. These rules are called strong rules.

7. Support and Confidence
• Support : It determines how frequent the rule is
applicable in the transaction set T.
 Let n be the number of transactions in T.
 Support = Support(XUY)/n
Ex : Consider the rule {Milk, Diapers} -> {Beer}
Support = 2/5 = 0.4
• Confidence : The confidence of a rule is the percentage of
transactions in T that contain X also contain Y.
Confidence = Support(XUY)/Support(X)
Confidence = 2/3 = 0.666

8. The Apriori Principle
• If an itemset is frequent, then all of its subsets must also
be frequent.
• Apriori is the first association rule mining algorithm that
pioneered the use of support-based pruning to
systematically control the exponential growth of candidate
itemsets.

9. Apriori Algorithm
Pseudo-code:
Ck: Candidate itemset of size k
Lk : frequent itemset of size k
L1 = {frequent items};
for (k = 1; Lk !=; k++) do begin
Ck+1 = candidates generated from Lk;
for each transaction t in database do
increment the count of all candidates in Ck+1
that are contained in t
Lk+1 = candidates in Ck+1 with min_support
end
return k Lk;

10. Frequent Itemset Generation

11. Rule Generation
The Apriori algorithm uses a level-wise approach for
generating association rules, where each level
corresponds to the number of items that belong to the
rule consequent.

12. Suppose {Bread,Diapers,Milk} is frequent, with sup=50%
Proper non-empty subsets are {Bread}, {Diapers}, {Milk},
{Bread, Diapers} ,{Bread, Milk}, {Diapers, Milk}
with sup=50%, 50%, 75%, 75%, 75%, 75% respectively.
The association rules generated are :
{Bread, Diapers} -> {Milk} Confidence = 66.666 %
{Diapers, Milk} -> {Bread} Confidence = 75%
{Bread, Milk} -> {Diapers} Confidence = 75%
{Bread} -> {Diapers, Milk} Confidence = 100%
{Milk} -> {Diapers, Bread} Confidence = 100%
{Diapers} -> {Bread, Milk} Confidence = 100%

13. Approaches for frequent itemset generation
BruteForce method :
Advantages :
• This method considers every k-itemset as a potential
candidate and then applies the candidate pruning step to
remove any unnecessary candidates.
Disadvantages :
• Candidate Pruning becomes extremely expensive because
a large number of itemsets must be examined.

14. Fk-1 X Fk-1 Itemset Generation
Advantages :
• This method merges a pair of frequent (k-1) itemsets
only if their first (k-2) are identical.
Disadvantages :
• This method requires an extra pruning step to ensure
that the remaining (k-2) subsets are frequent itemsets.

15. Fk-1 X F1 Itemset Generation
Advantages :
• This method takes frequent (k-1) itemsets and extends
them other frequent itemsets. For instance it takes 2-
frequent itemsets and combines them with frequent 1-
itemset.
Disadvantages :
• This method does not prevent the same candidate itemset
from being generated more than once.

16. Thank You