1. Mining Frequent Item Sets by Opportunistic Projection
Junqiang Liu1,4, Yunhe Pan1, Ke Wang2, Jiawei Han3
1 Institute of Artificial Intelligence, Zhejiang University, China
2 School of Computing Science, Simon Fraser University, Canada
3 Department of Computer Science, UIUC, USA
4 Dept. of CS, Hangzhou University of Commerce, China

2. Outline How to discover frequent item sets Previous works
Our approach: Mining Frequent Item Sets by Opportunistic Projection
Performance evaluations
Conclusions

3. What Are Frequent Items Sets
What is a frequent item set?
set of items, X, that occurs together frequently in a database, i.e., support(X) ≥ a given threshold
Example
Given support threshold 3, frequent item sets are as follows:
a:3, b:3, c:4, f :4, m:3, p:3,
ac:3, af :3, am:3, cf :3, cm:3, cp:3, fm:3,
acf :3, acm:3, afm:3, cfm:3,
acfm:3
tid
items 01
a c d f g i m p 02
a b c f l m o 03
b f h j o 04
b c k p s 05
a c e f l m n p

4. How To Discover Frequent Item Sets
Frequent item sets can be represented by a tree, which is not necessarily materailized.
Mining process:
a process of tree construction, accompanied by
a process of projecting transaction subsets

5. Frequent Item Set Tree - FIST
FIST is an ordered tree
each node: (item,weight)
the following are imposed
items ordered on a path (top-down)
items ordered at children (left to right)
Frequent item set
a path starting from the FIST root
its support is the ending node’s weight
PTS - projected transaction subset
Each FIST node has its own PTS, filtered or unfiltered
All transactions that support the frequent item set represented by the node

6. Frequent Item Set Tree (example)

7. Factors relate to Mining Efficiency and Scalability
The FIST construction strategy
breadth first v.s. depth first
The PTS representation
Memory-based representation: array-based, tree-based, vertical bitmap, horizontal bitstring, etc.
Disk-based representation
PTS projecting method and item counting method

8. Previous Works Research Strategy PTS Representation Projecting Method
Remarks
Apriori
breadth first
original DB
on the fly
Repetitive DB Scans
Huge FIST for dense
Exp. pattern matching
Tree
Projection
FPGrowth
depth first
FP-tree
recursively materialize conditional DB/Fptree
#of conditional FPtree in same order of mag. as # of fre. item sets
H-Mine
H-struct
partially materialize sub H-struct
Not most eff. for sparse
Call FP-Growth for dense
Partition for large
Depth
Project
horizontal bitstring
selective projection
Maximal fre. item sets
Less efficient than array-based for sparse & large
Less efficient than tree-based for dense
MAFIA
vertical bitmap
recursively materialize compressions

9. Our Approach: Mining Frequent Item Sets by Opportunistic Projection
Philosophy:
The algorithm must adapt the construction strategy of FIST, the representation of PTS, and the methods of item counting in and projection of PTSs to the features of PTSs.
Main points:
Mining sparse data by projecting array-based PTS
Intelligent projecting tree-based PTS for dense data
Heuristics for opportunistic projection

10. Mining sparse data by projecting array-based PTS
TVLA – threaded varied length array for sparse PTS
FIL– local frequent items list
LQ – linked queues
arrays
Each local frequent item has a FIL entry that consists of an item, a count, & a pointer.
Each transaction is stored in an array that is threaded to FIL by LQ according to the heading item in the imposing order.

11. How to project TVLA for PTS
Arrays (transactions) that support a node’s first child are threaded by the LQ attached to the first entry of FIL. (see previous figure)
TVLA for a child node’s PTS has its own FIL and LQ.
A child TVLA is unfiltered if it shares arrays with its parent, filtered otherwise.

12. How to project TVLA for PTS (cont.)
Get next child’s PTS by shifting transactions threaded in the LQ currently explored (current child’s PTS)

13. Intelligent projecting tree-based PTS for dense data
Tree-based Representation of dense PTS, inspired by FP-Growth
Novel projecting methods, totally differ from FP-Growth
Bottom up pseudo projection
Top down pseudo projection

14. Tree-based Representation of dense PTS
TTF - threaded transaction forest
IL - item list: each entry consists of an item, a count, and a pointer.
Forest: each node labeled by an item, associated with a weight.
Each local item in PTS has an entry in the IL.
Each transaction in the PTS is one path starting from a root in the forest.
count is the number of transactions represented by the path.
All nodes of the same item threaded by an IL entry.
TTF is filtered if only local frequent items appear in TTF, otherwise unfiltered.

15. Bottom up pseudo projection of TTF (example)

16. Top down pseudo projection of TTF (example)

17. Opportunistic Projection: Observations and Heuristics
Upper portion of a FIST can fit in memory.
Transactions’ Number that support length k item sets decreases sharply when k is greater than 2.
Heuristic 1:
Grow the upper portion of a FIST breadth first.
Grow the lower portion under level k depth first, whenever the reduced transaction set can be represented by a memory based structure, either TVLA or TTF.

18. Opportunistic Projection: Observations and Heuristics(2)
TTF compresses well at lower levels or denser branches, where there are fewer local frequent items in PTSs and the relative support is larger.
TTF is space expensive relative to TVLA if its compression ratio is less than 6-t/n ( t: number of transactions, n: number of items in a PTS).
Heuristic 2:
Represent PTSs by TVLA at high levels on FIST, unless the estimated compression ratio of TTF is sufficiently high.

19. Opportunistic Projection: Observations and Heuristics(3)
PTSs shrink very quickly at high levels or sparse branches on FIST where filtered PTSs are usually in form of TVLA.
PTSs at lower levels or dense branches shrink slowly where PTSs are represented by TTF. The creation of filtered TTF involves expensive pattern matching.
Heuristic 3:
Make a filtered copy for the child TVLA as long as there is free memory when projecting a parent TVLA.
Delimitate the pseudo child TTF first and then make a filtered copy if it shrinks substantially sharp when projecting a parent TTF.

20. Algorithm OpportuneProject
OpportuneProject(Database: D)
begin
create a null root for frequent item set tree T;
D’= BreadthFirst(T, D);
GuidedDepthFirst(root_of_T, D’);
end

21. Performance Evaluation: Efficiency on BMS-POS (sparse)

22. Performance Evaluation: Efficiency on BMS-WebView1 (sparse)

23. Performance Evaluation: Efficiency on BMS-WebView2 (sparse)

24. Performance Evaluation: Efficiency on Connect4 (dense)

25. Performance Evaluation: Efficiency on T25I20D100kN20kL5k

26. Performance Evaluation: Scalability on T25I20D1mN20kL5k

27. Performance Evaluation: Scalability on T25I20D10mN20kL5k

28. Performance Evaluation: Scalability on T25I20D100k~15mN20kL5k

29. Conclusions OpportuneProject
maximize efficiency and scalability for all data features by combining
depth first with breadth first search strategies
array-based and tree-based representation for projected transaction subsets
unfiltered, and filetered projections

30. Acknowledgement We would like to thank Blue Martini Software, Inc.
for providing us the BMS datasets!

31. References
[1] R. Agarwal, C. Aggarwal, and V. V. V. Prasad. A tree projection algorithm for generation of frequent itemsets. In Journal of Parallel and Distributed Computing (Special Issue on High Performance Data Mining), 2000.
[2] R. Agarwal, C. Aggarwal, and V. V. V. Prasad. Depth first generation of long patterns, in Proceedings of SIGKDD Conference, 2000.
[3] R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In SIGMOD’93, Washington, D.C., May 1993.
[4] R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In VLDB'94, pp , Santiago, Chile, Sept
[5] R.J.Bayardo. Efficiently mining long patterns from databases. In SIGMOD’98, pp , Seattle, Washington, June 1998.
[6] D.Burdick, M.Calimlim, J.Gehrke. MAFIA: A maximal frequent itemset algorithm for transactional databases. In proceedings of the 17th Internation Conference on Data Engineering, Heidelberg, Germany, April 2001.
[7] Sergey Brin, Rajeev Motwani, Jeffrey D. Ullman, Shalom Tsur. Dynamic Itemset Counting and Implication Rules for Market Basket Analysis. In SIGMOD’97, Tucson, AZ, May 1997.

32. References (2)
[8] J. Han and Y. Fu. Discovery of multiple-level association rules from large databases. In VLDB'95, Zuich, Switzerland, Sept
[9] J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation. In SIGMOD’2000, Dallas, TX, May 2000.
[10] D-I. Lin and Z. M. Kedem. Pincer-search: A new algorithm for discovering the maximum frequent set. In 6th Intl. Conf. Extending Database Technology, March 1998.
[11] J.S.Park, M.S.Chen, and P.S.Yu. An effective hash based algorithm for mining association rules. In Proc ACM-SIGMOD, , San Jose, CA, Feb
[12] J. Pei, J. Han, H. Lu, S. Nishio, S. Tang, and D. Yang, H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases, Proc Int. Conf. on Data Mining (ICDM'01)}, San Jose, CA, Nov
[13] Ashok Sarasere, Edward Omiecinsky, and Shamkant Navathe. An efficient algorithm for mining association rules in large databases. In 21st Int'l Conf. on Very Large Databases (VLDB), Zurich, Switzerland, Sept

33. References (3)
[14] H.Toivonen. Sampling large databases for association rules. In Proc Int. Conf. Very Large Data Bases (VLDB’96), , Bombay, India, Sept
[15] Zijian Zheng, Ron Kohavi and Llew Mason. Real World Performance of Association Rule Algorithms. In Proc Int. Conf. on Knowledge Discovery in Databases (KDD'01), San Francisco, California, Aug
[16]
[17]
[18]

34. Thank you !!!