Name
Title | ||
|---|---|---|
A pattern decomposition (PD) algorithm for finding all frequent patterns in large datasets |
Abstract | ||
|---|---|---|
Efficient algorithms to mine frequent patterns are crucial to many tasks in data mining. Since the Apriori algorithm was proposed (R. Agrawal and R. Srikant, 1994), there have been several methods proposed to improve its performance. However, most still adopt its candidate set generation-and-test approach. We propose a pattern decomposition (PD) algorithm that can significantly reduce the size of the dataset on each pass, making it more efficient to mine frequent patterns in a large dataset. The proposed algorithm avoids the costly process of candidate set generation and saves time by reducing dataset. Our empirical evaluation shows that the algorithm outperforms Apriori by one order of magnitude and is faster than FP-tree. Further, PD is more scalable than both Apriori and FP-tree |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1109/ICDM.2001.989603 | ICDM |
Keywords | Field | DocType |
pattern decomposition algorithm,apriori algorithm,frequent patterns,large datasets,fp-tree,pattern recognition,pattern decomposition,candidate set generation,set theory,large dataset,efficient algorithm,frequent pattern mining,proposed algorithm,data mining,frequent pattern,very large databases,candidate set generation-and-testapproach,algorithmoutperforms apriori,costly process,candidate set generation-and-test approach,association rules,computer science | Set theory,Data mining,Pattern recognition,Computer science,Apriori algorithm,A priori and a posteriori,Algorithm,FSA-Red Algorithm,Association rule learning,Artificial intelligence,Scalability | Conference |
ISBN | Citations | PageRank |
0-7695-1119-8 | 8 | 0.65 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qinghua Zou | 1 | 133 | 11.09 |
| Wesley W. Chu | 2 | 2311 | 789.42 |
| David Johnson | 3 | 8 | 0.65 |
| Henry Chiu | 4 | 26 | 2.39 |