In this tutorial, we will try to answer the following questions;

1. What are the Apriori candidate’s generations?
2. What is self-joining?
3. what is the Apriori pruning principle?

Apriori Candidates generation:

Candidates can be generated by the self joining and Apriori pruning principles.

Step 1:

self-joining

Example of self-joining

V W X Y ZX={V W X,    V W Y,    V X Y,    V X Z,    W X Y}Self-joining = X * XV  W X Y from V W X and V W YV X  Y  Z from V X Y and V X Z

So frequent candidates are V  W X Y  and V X  Y  Z

Step 2: 

Apriori pruning principle:

Example of Apriori pruning principle

V W X Y ZX={V W X,    V W Y,    V X Y,    V X Z,    W X Y} According to Apriori Pruning principle V X Y Z is removed because V Y Z is not in X. 

So frequent candidate is V W X Y

Apriori Candidates generation

Candidates can be generated by the self joining and Apriori pruning principles.

Step 1:

Self-joining of Apriori Candidates

Example of self-joining

A1 B1 C1 D1 E1

C1={A1 B1 C1,    A1 B1 D1,    A1 C1 D1,    A1 C1 E1,    B1 C1 D1}

Self-joining = C1 * C1A1  B1 C1 D1 from A1 B1 C1 and A1 B1 D1A1 C1  D1  E1 from A1 C1 D1 and A1 C1 E1

So frequent candidates are A1  B1 C1 D1  and A1 C1  D1  E1

Step 2: 

Apriori pruning principle

Example of Apriori pruning principle

A1 B1 C1 D1 E1C1={A1 B1 C1,    A1 B1 D1,    A1 C1 D1,    A1 C1 E1,    B1 C1 D1} According to Apriori Pruning principle A1 C1 D1 E1 is remoA1ed because A1 D1 E1 is not in C1. 

So frequent candidate is A1 B1 C1 D1.

Next Similar Tutorials

1. Frequent pattern Mining, Closed frequent itemset, max frequent itemset in data mining – Click Here
2. Support, Confidence, Minimum support, Frequent itemset, K-itemset, absolute support in data mining – Click Here
3. Apriori Algorithm in Data Mining with examples – Click Here
4. Apriori principles in data mining, Downward closure property, Apriori pruning principle – Click Here
5. Apriori candidates’ generations, self-joining, and pruning principles. – Click Here.

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Prof. Fazal Rehman Shamil

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