Name
Abstract | ||
|---|---|---|
Maximal frequent itemsets mining is one of the most fundamental problems in data mining In this paper, we present CfpMfi, a new depth-first search algorithm based on CFP-tree for mining MFI Based on the new data structure CFP-tree, which is a combination of FP-tree and MFI-tree, CfpMfi takes a variety pruning techniques and a novel item ordering policy to reduce the search space efficiently Experimental comparison with previous work reveals that, on dense datasets, CfpMfi prunes the search space efficiently and is better than other MFI Mining algorithms on dense datasets, and uses less main memory than similar algorithm. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-30549-1_42 | Australian Conference on Artificial Intelligence |
Keywords | Field | DocType |
experimental comparison,data mining,similar algorithm,new depth-first search algorithm,dense datasets,search space,maximal frequent itemsets mining,new data structure,fundamental problem,mfi mining algorithm,data structure,depth first search | Data structure,Data mining,Search algorithm,Economic order quantity,Computer science,Tree (data structure),Information extraction,Pruning | Conference |
Volume | ISSN | ISBN |
3339 | 0302-9743 | 3-540-24059-4 |
Citations | PageRank | References |
2 | 0.37 | 9 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuejin Yan | 1 | 25 | 3.06 |
| Zhoujun Li | 2 | 964 | 115.99 |
| Tao Wang | 3 | 33 | 3.91 |
| Yuexin Chen | 4 | 4 | 1.08 |
| Huo-wang Chen | 5 | 235 | 33.47 |