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Apriori algorithm

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Ashis Kumar Chanda
Ashis Kumar Chanda

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

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1 I NAME OF PRESENTER Apriori Algorithm Ashis Kumar Chanda Department of Computer Science and Engineering University of Dhaka
2 I NAME OF PRESENTERCSE, DU2 Key concepts oIntroduction oFrequent Itemsets oApriori Property oJoin operation oPrune operation oDrawback oImproving mechanism
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 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 I NAME OF PRESENTERCSE, DU5 Original dataset
6 I NAME OF PRESENTERCSE, DU6 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 I NAME OF PRESENTERCSE, DU7 Finding support count Fig: Result after scanning database first time
8 I NAME OF PRESENTERCSE, DU8 Finding l1 Fig: Result after considering minimum support
9 I NAME OF PRESENTERCSE, DU9 Finding c2 Fig: Result after L1*L1 join step
10 I NAME OF PRESENTERCSE, DU10 Finding L2 Fig: Result after pruning step of C2 dataset
11 I NAME OF PRESENTERCSE, DU11 Finding C3 Fig: Result after L2*L2 join step
12 I NAME OF PRESENTERCSE, DU12 Finding L3 Fig: Result after pruning step of C3 dataset
13 I NAME OF PRESENTERCSE, DU13 Uses GSP(Generalized Sequential Patterns) Spade(Sequential Pattern Discovery using Equivalent classes)
14 I NAME OF PRESENTERCSE, DU14 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 I NAME OF PRESENTERCSE, DU15 Improving mechanism  Hash based technique  Transaction reduction  Partitioning  Sampling  Dynamic itemset counting
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

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Apriori algorithm

  • 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
  • 6. 6 I NAME OF PRESENTERCSE, DU6 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
  • 8. 8 I NAME OF PRESENTERCSE, DU8 Finding l1 Fig: Result after considering minimum support
  • 9. 9 I NAME OF PRESENTERCSE, DU9 Finding c2 Fig: Result after L1*L1 join step
  • 10. 10 I NAME OF PRESENTERCSE, DU10 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
  • 13. 13 I NAME OF PRESENTERCSE, DU13 Uses GSP(Generalized Sequential Patterns) Spade(Sequential Pattern Discovery using Equivalent classes)
  • 14. 14 I NAME OF PRESENTERCSE, DU14 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