1. Data Mining Association Rules Yao Meng Hongli Li 91.574 Database II Fall 2002

2. Outline Overview  Apriori  AprioriTid  DIC Data Structure Experiment Environment Experiment Result and Analysis

3. Overview – Apriori Algorithm

4. Overview – AprioriTid

5. Overview – DIC  Read M transaction  Increment those itemset that are current counting  If all the child of a itemset turned to large, begin to counting this itemset  If an itemset has been counted through all the transaction, remove it from the current counting list  If at the end of the DB, go to the first step  Stop if no itemset are need to counting

6. Hypothesis of Performance Analysis Given a memory size AprioriTid generally has better performance than Apriori due to I/O saving DIC has better performance than Apriori in fairly homogenenous data environment. DIC performance should approach that of Apriori while M approaches number of total transaction.

7. Experiment Environment Data Sets  IBM Synthetic Dataset Generation Code for Association Rules Enviroments  Operating System: Microsoft Windows XP Professional  Computer Intel Pentium III processor 550MHz RAM 384 MB  Source code written in Java

8. Data Structure Apriori and DIC  Candidate Itemset stored in a hash-tree  Each internal node is are hashtables  The leaves stored the candidate itemset AprioriTid  Use array to keep candidates

9. Size vs. Execution Time Number of Items = 8 Avg transaction length = 5 M = 500

10. Support Threshold Size = 16410 transactionNumber of Items = 8 Average Length per transaction = 5M = 500

11. DIC – Different M value Size = 12291 transactionNumber of Items = 8 Average Length per transaction = 5

12. DIC – “Non-Homogeneous” Dataset Size = 6000 transactionNumber of Items = 8 M = 500

13. Conclusions AprioriTid is the best in our experiment  I/O saving  AprioriTid use small Data structure Apriori and DIC are very similar  Apriori is Special Case of DIC  They use same data structure DIC  Sensitive to data  M affects performance

14. Reference 1. Rakesh Agrawal, Tomasz Imielinski, Arun Swami. Mining Association Rules between Sets of Items in Large Database. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, 1993 2. Rakesh Agrawal, Ramakrishnan Srikant. Fast Algorithms for Mining Association Rules. Proc. 20th Int. Conf. Very Large Data Bases, VLDB, page 487-499. 1994 3. Ashok Savasere, Edward Omiecinski, Shamkant Navathe. An Efficient Algorithm for Mining Association Rules in Large Databases. Proc. of the 21st VLDB Conf., pp. 432- 444, 1995. 4. Sergey Brin, Rajeev Motwani, Jeffrey D. Ullman, Shalom Tsur. Dynamic Itemset Counting and Implication Rules for Market Basket Data. SIGMOD 1997, Proceedings ACM SIGMOD International Conference on Management of Data. Tucson, Arizona, USA. 1997. 5. J. Hipp, U. Güntzer, G. Nakhaeizadeh. Mining Association Rules: Deriving a Superior Algorithm by Analysing Today's Approaches. Proceedings of the 4th European Symposium on Principles of Data Mining and Knowledge Discovery (PKDD '00), Lyon, France. 2000. 6. Jochen Hipp, Ulrich Güntzer, Gholamreza Nakhaeizadeh. Algorithms for Association Rule Mining – A General Survey and Comparison. SIGKDD Explorations. 2(1): 58-64. 2000. 7. R. Srikant, R. Agrawal. Mining Generalized Association Rule. In Proc. of the VLDB Conference, September 1995