Apriori algorithm is one of the best algorithm in Data Mining field that used to find frequent item-sets. The apriori property tells us that all non-empty subsets of a frequent itemset must also be frequent. This algorithm is proposed by R. Agrawal and R. Srikant

1. 1 I NAME OF PRESENTER
Apriori Algorithm
Ashis Kumar Chanda
Department of Computer Science and Engineering
University of Dhaka

2. 2 I NAME OF PRESENTERCSE, DU2
Key concepts
oIntroduction
oFrequent Itemsets
oApriori Property
oJoin operation
oPrune operation
oDrawback
oImproving mechanism

3. 3 I NAME OF PRESENTERCSE, DU3
Introduction
• Extracting hidden knowledge or pattern from
huge data is know as Data mining
• Find frequent itemsets, closed itemsets,
periodic patterns, assertion rule
• The First and main algorithm of Data mining
is Apriori to find frequent itemsets

4. 4 I NAME OF PRESENTER
Apriori property: All nonempty subsets of a frequent
itemset must also be frequent
There is two steps:
1. The join step: To find 𝐿 𝑘, a set of candidate k-
itemsets is generated by joining 𝐿 𝑘 with itself
2. The prune step: 𝐶 𝑘 is a superset of 𝐿 𝑘, that is, its
members may or may not be frequent, but all of the
frequent k-itemsets are included in 𝐶 𝑘. A scan of the
database to determine the count of each candidate in 𝐶 𝑘
would result in the determination of 𝐿 𝑘
CSE, DU4
Algorithm

5. 5 I NAME OF PRESENTERCSE, DU5
Original dataset

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Customized dataset
Assuming
Mango=M Onion=O Nintendo=N Key-chain=K
Eggs=E Yo-yo=Y Doll=D Apple=A
Umbrella=U Corn=C Ice-cream=I
Considering each event with an unique character, we get
the database in a short view that given below

7. 7 I NAME OF PRESENTERCSE, DU7
Finding support count
Fig: Result after scanning database first
time

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Finding l1
Fig: Result after considering minimum
support

9. 9 I NAME OF PRESENTERCSE, DU9
Finding c2
Fig: Result after L1*L1 join step

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Finding L2
Fig: Result after pruning step of C2
dataset

11. 11 I NAME OF PRESENTERCSE, DU11
Finding C3
Fig: Result after L2*L2 join step

12. 12 I NAME OF PRESENTERCSE, DU12
Finding L3
Fig: Result after pruning step of C3
dataset

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Uses
GSP(Generalized Sequential Patterns)
Spade(Sequential Pattern Discovery using
Equivalent classes)

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Drawback
 Huge candidate set generation
Every event joins with all other events. If there is
‘e’ events in ith step, then total generated
candidate sets are: e*e
 Repeatedly scan the database
In every steps, this process need to scan whole
database to find frequency of a event

15. 15 I NAME OF PRESENTERCSE, DU15
Improving mechanism
 Hash based technique
 Transaction reduction
 Partitioning
 Sampling
 Dynamic itemset counting

16. 16 I NAME OF PRESENTERCSE, DU16
References
- Data Mining Concepts & Techniques
by J. Han & M. Kamber
- Database system Concept
by Abraham Sillberschatz, Korth, Sudarshan
- Lecture of Dr. S. Srinath
Institute of Technology at Madras, India