The document discusses market basket analysis and two algorithms for conducting association rule mining on transaction data: Apriori and FP Growth. Apriori works by joining all item combinations and pruning those below a minimum support threshold, generating association rules from frequent itemsets. FP Growth avoids joining by building a frequent pattern tree and mining it to extract frequent patterns more efficiently. The document provides an example application of each algorithm on sample transaction data to find the most frequently purchased item combinations.

2. MARKET BASKET ANALYSIS

3. MARKET BASKET ANALYSIS
A market basket analysis or
recommendation engine is what is behind all these
recommendations we get when we go shopping online
or whenever we receive targeted advertising. The
underlying engine collects information about people’s
habits and knows that if people buy pasta and wine,
they are usually also interested in pasta sauces. So, the
next time you go to the supermarket and buy pasta
and wine, be ready to get a recommendation for some
pasta sauce!.

4. About MBA:
The first one is antecedent(previous) and
the second is consequent(resultant) and few
measures such as support, confidence and lift,
define how reliable the rule is this, so the most
famous algorithm generating these rules in the
1. Apriori algorithm.
2. FP growth.
3. Partitioning method, etc.

5. Association Rule Mining
ARM has transaction data that contains sequence of
PRODUCT_ID in fictitious baskets which also contains
PRODUCT_INFO, NAME and PRICE.

6. Now we are going to see
•APRIORI ALGORITHM
•FP GROWTH

7. 1. APRIORI ALGORITHM

8. It has two steps, Join & Prune and it has minimum support count: 2
This is sample data ( ‘T’ is transaction data, ‘T1’ to ‘T9’).
T1  1, 2, 5
T2  2, 4
T3  2, 3
T4  1, 2, 4
T5  1, 3
T6  2, 3
T7  1, 3
T8  1, 2, 3, 5
T9  1, 2, 3
Transaction Items

9. Join step:
Here u have to do cross join the five items as, 1 with 1, 1 with 2, 1 with 3,
1 with 4, 1 with 5, 2 with 3, 2 with 4, 2 with 5, 3 with 4, 3 with 5, 4 with 5.
1. T1  1, 2, 5
2. T2  2, 4
3. T3  2, 3
4. T4  1, 2, 4
5. T5  1, 3
6. T6  2, 3
7. T7  1, 3
8. T8  1, 2, 3, 5
9. T9  1, 2, 3
Prune step:
• Cut down method, first we count all the five item in all the transaction.
I1  6
I2  7
I3  6
I4  2
I5  2
• This are all the minimum support count: 2

10. Using the CROSS DOWN STEP arranging I1 to I5 and sort using minimum support cost: 2.
I1 I2  4 I1 I2  4
I1 I3  4 I1 I3  4
I1 I4  1 I1 I5  2
I1 I5  2 I2 I3  4
I2 I3  4 I2 I4  2
I2 I4  2 I2 I5  2
I2 I5  2
I3 I4  0
I3 I5  1
I4 I5  0
Step: 1
1. T1  1, 2, 5
2. T2  2, 4
3. T3  2, 3
4. T4  1, 2, 4
5. T5  1, 3
6. T6  2, 3
7. T7  1, 3
8. T8  1, 2, 3, 5
9. T9  1, 2, 3

11. Step: 2
Now again sorted list CROSS JOIN STEP arranging I1, I2, I3, I4 & I5, from the previous step,
I1 I2 I3  2
I1 I2 I4  1
I1 I2 I5  2
I1 I3 I4  0
I1 I3 I5  1
I1 I4 I5  0
I2 I3 I4  1
I2 I4 I5  0
I2 I1 I3  1
I2 I1 I4  0, it will continue as more possibilities.

12. Result:
• So this is the frequent patterns of purchase item, my customer will purchase the maximum
item.
I1 I2 I3  2
I1 I2 I5  2
• These two combination are the max purchase item find using “APRIORIY ALGORITHM”.
• Even it has a disadvantages because of so many useless combination of items, again and
again.
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13. 2. FP GROWTH

14. FP growth stands for ‘Frequent Pattern Growth’
This is the sample data ( ‘T’ is transaction data, ‘T1’ to ‘T9’ ) and getting the number of each item
placed in the transaction and converted into descending. It has minimum support count: 2.
1. T1  1, 2, 5
2. T2  2, 4
3. T3  2, 3
4. T4  1, 2, 4
5. T5  1, 3
6. T6  2, 3
7. T7  1, 3
8. T8  1, 2, 3, 5
9. T9  1, 2, 3
Number of item placed Descending placed item
I1  6 I2  7
I2  7 I1  6
I3  6 I3  6
I4  2 I4  2
I5  2 I5  2
Step: 1

15. NULL
I2
I3
I1
I4
I5 I4
I3
I5
I1
I3
1 + 1 + 1 + 1 + 1 + 1 + 1 =
1 + 1 + 1 + 1 =
= 1 + 1
1 + 1 =
1 + 1 =
= 1 + 1
= 1
= 1
= 1
1 =
Step: 2
1. T1  1, 2, 5
2. T2  2, 4
3. T3  2, 3
4. T4  1, 2, 4
5. T5  1, 3
6. T6  2, 3
7. T7  1, 3
8. T8  1, 2, 3, 5
9. T9  1 , 2, 3

16. Step: 3
• Start from the item has minimum support value (i.e.) – I5.
• Exclude or eliminate from the item has maximum support value (i.e.) – I2.
CBP CFP
I5  { I1, I2  1 } { I1, I2, I3  1 }  { I2  2 } { I3  1 } { I1  2}
I4  { I1, I2  1 } { I2  1 }  { I2  2 } { I1  1 }
I3  { I1  2 } { I2  2 } { I2, I1  2 }  { I2  4 } { I1  2 } { I1  2 }
I1  { I2  4 }  { I2  4 }
CBP – Conditional Base Pattern
** Condition: To reach a item we should need a path, we can’t reach directly from NULL.

17. Result:
The combination of I5, I4, I3, I1 obtained from CFP, the resultant FREQENT PATTERN are
I5  { I2, I5  2 } , { I1, I5  2 } , { I2, I1, I5  2} = 3 Combination.
I4  { I2, I4  2 } = 1 Combination.
I3  { I2, I3  4 } , { I1, I3  4 } , { I1, I2, I3  2 } = 3 Combination.
I1  { I2, I1  4 } = 1 Combination.
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18. -- THANK YOU --