Name
Abstract | ||
|---|---|---|
Maximal frequent itemsets (MFIs) mining is important for many applications. To improve the performance of the MFI algorithms, the key is to use appropriate pruning techniques which can maximally reduce the searching space of the algorithm. In this paper, we present a novel pruning technique, subset equivalence pruning. To mining MFIs in data streams, we reconstruct the FPmax* algorithm to a single-pass algorithm, named FPmax*-DS. Subset equivalence pruning technique is added in FPmax*-DS. The experiments show that the pruning technique can efficiently reduce the searching space. Especially for some dense datasets, the size of searching space can be trimmed off by about 40%. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/FSKD.2007.102 | FSKD (3) |
Keywords | Field | DocType |
single-pass algorithm,subset equivalence pruning technique,mining mfis,novel pruning technique,dense datasets,pruning technique,mfi algorithm,mining maximal frequent itemsets,subset equivalence pruning,appropriate pruning technique,data stream,set theory,search space,data mining | Data mining,Data stream mining,Killer heuristic,Computer science,Equivalence (measure theory),Artificial intelligence,Null-move heuristic,Pruning,Set theory,Pattern recognition,Principal variation search,Pruning (decision trees),Machine learning | Conference |
ISBN | Citations | PageRank |
0-7695-2874-0 | 0 | 0.34 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fujiang Ao | 1 | 17 | 4.79 |
| Yuejin Yan | 2 | 25 | 3.06 |
| Jian Huang | 3 | 2608 | 200.50 |
| Kedi Huang | 4 | 79 | 11.95 |