1. Mining Association Rules from Stars
Department of Information & Computer Education, NTNU
Mining Association Rules from Stars
Eric Ka Ka Ng, Ada Wai-Chee Fu, and Ke Wang, 2002 IEEE International Conference on Data Mining (ICDM'02), December , Maebashi City, Japan.
Advisor：Jia-Ling Koh
Speaker：Chen-Yi Lin

2. Outline Introductions Problem Definition The Proposed Method
Department of Information & Computer Education, NTNU
Outline
Introductions
Problem Definition
The Proposed Method
Experimental Results
Conclusions

3. Department of Information & Computer Education, NTNU
Introductions
In real life, a database is typically made up of multiple tables and one important case is where some of the tables form a star schema.
Dimension table
Fact table (FT)

4. Problem Definition (1/2)
Department of Information & Computer Education, NTNU
Problem Definition (1/2)
Dimension table contains primary key (tid), some other attributes and no foreign keys.
The attributes in the dimension tables are unique.
The attributes take categorical values.
Fact table (FT)
stores the tids from dimension tables as foreign keys.

5. Problem Definition (2/2)
Department of Information & Computer Education, NTNU
Problem Definition (2/2)
categorical value
tid
Dimension table and its binary representation

6. The Proposed Method (1/8)
Department of Information & Computer Education, NTNU
The Proposed Method (1/8)
tid_list is an ordered list of elements of the form tid(count).
: e.g.

7. The Proposed Method (2/8)
Department of Information & Computer Education, NTNU
The Proposed Method (2/8)
Minsup=5
count=6
count=5
Hence the itemset is frequent

8. The Proposed Method (3/8)
Department of Information & Computer Education, NTNU
The Proposed Method (3/8)
Binding multiple Dimension Tables
(1) To assign each combination of tid from A and tid from B in FT a new tid
(2) and to set the tid in the tid_lists for items in AB to the corresponding new tid.

9. The Proposed Method (4/8)
Department of Information & Computer Education, NTNU
The Proposed Method (4/8)
The set of frequent itemsets with
items from tables A and/or B
The set of frequent itemsets
with items from tables A
An example of “binding” order

10. The Proposed Method (5/8)
Department of Information & Computer Education, NTNU
The Proposed Method (5/8)
(1)
(2)

11. The Proposed Method (6/8)
Department of Information & Computer Education, NTNU
The Proposed Method (6/8)
The fact table FT is scanned once and the information is stored into a data structure
Prefix Tree
each node has a label (a tid) and a counter.

12. The Proposed Method (7/8)
Department of Information & Computer Education, NTNU
The Proposed Method (7/8)
counter
tid
Prefix tree structure representing

13. The Proposed Method (8/8)
Department of Information & Computer Education, NTNU
The Proposed Method (8/8)
Collapsing the prefix tree

14. Experimental Results (1/5)
Department of Information & Computer Education, NTNU
Experimental Results (1/5)
All experiments are conducted on SUN Ultra-Enterprise Generic_ with SunOS 5.7 and 8192MB Main Memory.
Programs are written in C++.

15. Experimental Results (2/5)
Department of Information & Computer Education, NTNU
Experimental Results (2/5)
In the first dataset, items in A and B are strongly related, such that frequent itemsets contain items across A and B, while items in C are not involved.
In the second dataset, items in A, B and C are all strongly related, so that maximal frequent itemsets always contain items from all of A, B and C.

16. Experimental Results (3/5)
Department of Information & Computer Education, NTNU
Experimental Results (3/5)
masl: implementing tid_list as a linked list structure
masb: implementing tid_list as a fixed-size bitmap and an array of count
fpt: the join-before-mine approach with FP-tree algorithm [HPY00]
Running time for (A, B) related and (A, B, C) related datasets

17. Experimental Results (4/5)
Department of Information & Computer Education, NTNU
Experimental Results (4/5)
Mixture datasets
10% of transactions contain frequent itemsets from only A, B, C, respectively.
15% contain frequent itemsets from AB, BC, AC, respectively.
10% contain frequent itemsets from ABC.
15% are random noise.

18. Experimental Results (5/5)
Department of Information & Computer Education, NTNU
Experimental Results (5/5)
Running time for mixture datasets

19. Department of Information & Computer Education, NTNU
Conclusions
In the paper, the proposed method is a new algorithm for mining association rules on a star schema without performing the natural join.
The proposed method can be generalized to be applied to a snowflake structure.