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Apriori Algorithm Review for Finals.

Apriori Algorithm Review for Finals. . SE 157B, Spring Semester 2007 Professor Lee By Gaurang Negandhi. Overview . Definition of Apriori Algorithm Steps to perform Apriori Algorithm Apriori Algorithm Examples Pseudo Code for Apriori Algorithm Apriori Advantages/Disadvantages References.

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Apriori Algorithm Review for Finals.

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  1. Apriori Algorithm Review for Finals. SE 157B, Spring Semester 2007 Professor Lee By Gaurang Negandhi

  2. Overview • Definition of Apriori Algorithm • Steps to perform Apriori Algorithm • Apriori Algorithm Examples • Pseudo Code for Apriori Algorithm • Apriori Advantages/Disadvantages • References

  3. Definition of Apriori Algorithm • In computer science and data mining, Apriori is a classic algorithm for learning association rules. • Apriori is designed to operate on databases containing transactions (for example, collections of items bought by customers, or details of a website frequentation). • The algorithm attempts to find subsets which are common to at least a minimum number C (the cutoff, or confidence threshold) of the itemsets.

  4. Definition (contd.) • Apriori uses a "bottom up" approach, where frequent subsets are extended one item at a time (a step known as candidate generation, and groups of candidates are tested against the data. • The algorithm terminates when no further successful extensions are found. • Apriori uses breadth-first search and a hash tree structure to count candidate item sets efficiently.

  5. Steps to Perform Apriori Algorithm

  6. Apriori Algorithm ExamplesProblem Decomposition If theminimum support is 50%, then {Shoes, Jacket} is the only 2- itemset that satisfies the minimum support. If the minimum confidence is 50%, then the only two rules generated from this 2-itemset, that have confidence greater than 50%, are: Shoes  Jacket Support=50%, Confidence=66% Jacket  Shoes Support=50%, Confidence=100%

  7. Database D L1 C1 Scan D C2 C2 L2 Scan D L3 C3 Scan D The Apriori Algorithm — Example Min support =50%

  8. Pseudo Code for Apriori Algorithm

  9. Apriori Advantages/Disadvantages • Advantages • Uses large itemset property • Easily parallelized • Easy to implement • Disadvantages • Assumes transaction database is memory resident. • Requires many database scans.

  10. Summary • Association Rules form an very applied data mining approach. • Association Rules are derived from frequent itemsets. • The Apriori algorithm is an efficient algorithm for finding all frequent itemsets. • The Apriori algorithm implements level-wise search using frequent item property. • The Apriori algorithm can be additionally optimized. • There are many measures for association rules.

  11. References • References • Agrawal R, Imielinski T, Swami AN. "Mining Association Rules between Sets of Items in Large Databases." SIGMOD. June 1993, 22(2):207-16, pdf. • Agrawal R, Srikant R. "Fast Algorithms for Mining Association Rules", VLDB. Sep 12-15 1994, Chile, 487-99, pdf, ISBN 1-55860-153-8. • Mannila H, Toivonen H, Verkamo AI. "Efficient algorithms for discovering association rules." AAAI Workshop on Knowledge Discovery in Databases (SIGKDD). July 1994, Seattle, 181-92, ps. • Implementation of the algorithm in C# • Retrieved from "http://en.wikipedia.org/wiki/Apriori_algorithm"

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