Name
Abstract | ||
|---|---|---|
Mining maximal frequent itemsets in data streams is more difficult than mining them in static databases for the huge, high-speed and continuous characteristics of data streams. In this paper, we propose a novel one-pass algorithm called FpMFI-DS, which mines all maximal frequent itemsets in Landmark windows or Sliding windows in data streams based on FP-Tree. A new structure of FP-Tree is designed for storing all transactions in Landmark windows or Sliding windows in data streams. To improve the efficiency of the algorithm, a new pruning technique, extension support equivalency pruning (ESEquivPS), is imported to it. The experiments show that our algorithm is efficient and scalable. It is suitable for mining MFIs both in static database and in data streams. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-73499-4_36 | MLDM |
Keywords | Field | DocType |
extension support equivalency pruning,mining mfis,landmark windows,static database,mining maximal frequent itemsets,static databases,sliding windows,new pruning technique,maximal frequent itemsets,data stream,new structure,sliding window | Data mining,Data stream mining,Pattern recognition,Computer science,Artificial intelligence,Landmark,Machine learning,Scalability,Pruning | Conference |
Volume | ISSN | Citations |
4571 | 0302-9743 | 2 |
PageRank | References | Authors |
0.36 | 15 | 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 |