1. Seminar Association Rules Mining-Apriori

2.

• Study on Application of Apriori Algorithm in Data Mining

• 2010 Second International Conference on Computer Modeling and Simulation

• Yanxi Liu

• 2010 IEEE

3.

• Association rule mining:

• • Finding frequent patterns, associations, correlations, or

• Applications:

• • Basket data analysis, cross-marketing, catalog design, loss-leader analysis, clustering, classification, etc.

• Examples.

• • Rule form: Body ead [support, confidence].

• • buys(x, diapers) buys(x, beers) [0.5%, 60%]

• • major(x, CS) ^ takes(x, DB) grade(x, A) [1%, 75%]

4.

• Given: (1) database of transactions, (2) each transaction is a list of items (purchased by a customer in a visit)

• Find: all rules that correlate the presence of one set of items with that of another set of items

• • E.g., 98% of people who purchase tires and auto accessories also get automotive services done

• Applications

• • * Maintenance Agreement (What the store should do to boost Maintenance Agreement sales)

• • Home Electronics * (What other products should the store stocks up?)

• • Attached mailing in direct marketing

• • Detecting ping-ponging of patients , faulty collisions

5.

• Apriori algorithm is one of the most influential

• algorithms to mine the frequent item sets of Boolean association rules.

6.

7.

• Mining Association Rules Using Fast Algorithm

• M.Anandhavalli & Sandip Jain

• 2010 IEEE

8.

• Efficiently Using Matrix in Mining Maximum Frequent Itemset

• 2010 Third International Conference on Knowledge Discovery and Data Mining

• Liu Zhen-yu

• 2010 IEEE

9.

• An Encounter with Strong Association Rules

• G. S. Bhamra

• 2010 IEEE

10. Title

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11. Title

12. Application