Name
Abstract | ||
|---|---|---|
Efficient algorithms to mine frequent patterns are crucial to many tasks in data mining. Since the Apriori algorithm was proposed in 1994, there have been several methods proposed to improve its performance. However, most still adopt its candidate set generation-and-test approach. In addition, many methods do not generate all frequent patterns, making them inadequate to derive association rules. 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 all frequent patterns in a large dataset. The proposed algorithm avoids the costly process of candidate set generation and saves time by reducing the size of the dataset. Our empirical evaluation shows that the algorithm outperforms Apriori by one order of magnitude and is faster than FP-tree algorithm. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1007/s101150200016 | Knowl. Inf. Syst. |
Keywords | Field | DocType |
Keywords: Data mining,Frequent pattern,Itemset,Pattern decomposition | Data mining,Pattern recognition,Computer science,GSP Algorithm,A priori and a posteriori,Apriori algorithm,Algorithm,FSA-Red Algorithm,Association rule learning,Artificial intelligence,Machine learning | Journal |
Volume | Issue | Citations |
4 | 4 | 12 |
PageRank | References | Authors |
0.89 | 14 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qinghua Zou | 1 | 133 | 11.09 |
| Wesley Chu | 2 | 14 | 2.19 |
| David Johnson | 3 | 153 | 11.00 |
| Henry Chiu | 4 | 26 | 2.39 |