1. Implementation of “A New Two-Phase Sampling Based Algorithm for Discovering Association Rules” Tokunbo Makanju Adan Cosgaya Faculty of Computer Science Dalhousie University Fall 2005 CSCI 6405 Data Warehousing and Data Mining

2. Overview Introduction Algorithm Data Preparation Experimental Results Conclusions References

3. Introduction Size of datasets are getting larger The time required to mine information from these datasets increases as datasets get larger Demand for faster rule mining Solution: mine a sample of the original dataset

4. Algorithm FAST (Finding Association in Sample Transactions) 2 versions  FAST-Trim  FAST-Grow FAST outline:  Obtain a simple random sample S  Compute frequency for each 1-itemset  Obtain a reduced sample S 0 from S by either trimming S or growing S 0.  Run a standard association-rule algorithm against S 0

5. Algorithm Distance Functions I 1 (T) = set of all 1-itemsets in transaction set T L 1 (T) = set of frequent 1-itemsets in transaction set T f(A;T) = support of itemset A in transaction set T

6. Algorithm Obtain a simple random sample S from D compute f(A;S) from each A element of S set i=0, S 0 (i)= , minDist =  , and minStage=-1; while (|S 0 | < n) { divide S 0 into disjoint groups of min(k,| S-S 0 |) transactions each; for each group G { set S 0 = S 0 (i)  {t*}, where Dist(S 0 (i)  {t*},S) = min Dist(S 0 (i)  {t},S) } compute f(A; S 0 (i)) for each item A element of S 0 ; if (Dist( S 0 (i),S) < minDist) { set minDist := dist ( S 0 ( i), S) and minStage := i; } set S 0 (i + 1 / := S0(i); } FAST-Grow Algorithm

7. Data Preparation Downloaded from fimi.cs.helsinki.fi/data/accidents.pdf fimi.cs.helsinki.fi/data/accidents.pdf The data source for this dataset is the National Institute of Statistics from the region of Flanders in Belgium. In total 572 unique attribute values can be found in the dataset and an average of 45 attribute values are recorded for each accident.

8. Experimental Results Dataset with 340,183 transactions Obtained a reduced sample of 30% Final sample ratios of 2.5%, 5%, 7.5% and 10% Parameters:  Minimum Support = 0.77%  Size of group k = 10

9. Experimental Results Sampling ratio# of rules produced% of Accuracy 2.5%(8,500 transactions)294927.64% 5%(17,010 transactions)585100% 7.5%(25,500 transactions)445100% 10%(34,020 transactions)585100% Results

10. Conclusions No need to process a large input dataset FAST- grow can achieve a high accuracy even with a small sampling ratio of 5-10% The algorithm has a better performance when using the fixed-size stopping criterion

11. References [1] B. Chen, P. Haas, and P. Scheuermann. A new two-phase sampling based algorithm for discovering association rules. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2002 [2] H. Bronnimann, B. Chen, P. Haas, M. Dash, Y. Qiao, P. Scheuermann, Efficient Data-Reduction Methods for On-Line Association Rule Discovery. Presented at NSF Workshop on Next-Generation Data Mining (NGDM02), November 2002. [3] K. Geurts. Traffic Accidents Data Set. fimi.cs.helsinki.fi/data/accidents.pdf.fimi.cs.helsinki.fi/data/accidents.pdf Last Access: 17/11/2005 [4] GNU publicly available implementation of Apriori algorithm, written by Christian Borgelt. http://fuzzy.cs.uni-magdeburg.de/~borgelt/software.htmlhttp://fuzzy.cs.uni-magdeburg.de/~borgelt/software.html Last Access: 24/11/2005

12. Thank you! Questions?