Name
Title | ||
|---|---|---|
Ramp: high performance frequent itemset mining with efficient bit-vector projection technique |
Abstract | ||
|---|---|---|
Mining frequent itemset using bit-vector representation approach is very efficient for small dense datasets, but highly inefficient for sparse datasets due to lack of any efficient bit-vector projection technique. In this paper we present a novel efficient bit-vector projection technique, for sparse and dense datasets. We also present a new frequent itemset mining algorithm Ramp (Real Algorithm for Mining Patterns) using bit-vector representation approach and our bit-vector projection technique. The performance of the Ramp is compared with the current best frequent itemset mining algorithms. Different experimental results on sparse datasets show that mining frequent itemset using Ramp is faster than the current best algorithms. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11731139_59 | PAKDD |
Keywords | Field | DocType |
novel efficient bit-vector projection,sparse datasets,frequent itemset,new frequent itemset mining,dense datasets,mining algorithm,bit-vector representation approach,efficient bit-vector projection technique,high performance,current best frequent itemset,bit-vector projection technique | Data mining,Computer science,Association rule learning,Knowledge extraction,Data mining algorithm,Bit array | Conference |
Volume | ISSN | ISBN |
3918 | 0302-9743 | 3-540-33206-5 |
Citations | PageRank | References |
4 | 0.48 | 9 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shariq Bashir | 1 | 167 | 13.48 |
| Abdul Rauf Baig | 2 | 126 | 15.82 |