Explain APRIORI PRINCIPLE.

 APRIORI PRINCIPLE

  • The Apriori principle states that if an item set is frequent, then all of its subsets must also be frequent. For example, if (Rubber, Book, pencil) is frequent, so is [Rubber, Book), [Rubber. pencil], [Book, pencil], [Rubber), (Book), [(pencil). The Apriori principle holds due to the following property of the support measure:-
  • VX, Y: (XY)s(X) 2 s(Y) Where, X and Y are itemsets, that is, Support of an itemset never exceeds the support of its subsets. This property is also known as anti-monotone or property of support.
  • For example, consider the dataset D shown in table 5.1 above. Based on the given dataset in table 5.1 support of itemset (Pencil) 4 which is greater than the support of superset itemset (Pencil, Rubber) = 2. Similarly, support (Paper)> support (Pencil, Paper) and support (Book, Rubber) > support (Book, Rubber, Ruler) also holds. 
  • This property is used by the Apriori algorithm to reduce the complexity of the candidate generation as: "If there is any item set which is infrequent, its superset should not be generated or tested." For example, if item-set (a, b) is infrequent then we do not need to take into account all its super-sets (a, b, c). (a, b, d) and (a, b, c, d]. That these supersets of infrequent itemset (a, b) are pruned by the Apriori algorithm. Hence, this elimination helps to improve the itemset generation process.


Comments

Popular posts from this blog

Short note on Uniform Gradient Cash Flow and PERT

Discuss different JavaFX layouts with suitable example.

What is the cloud cube model? Explain in context to the Jericho cloud cube model along with its various dimensions.