Name
Abstract | ||
|---|---|---|
This paper discusses a novel communication efficient distributed algorithm for approximate mining of frequent patterns from transactional databases. The proposed algorithm consists in the distributed exact computation of locally frequent itemsets and an effective method for inferring the local support of locally unfrequent itemsets. The combination of the two strategies gives a good approximation of the set of the globally frequent patterns and their supports. Several tests on publicly available datasets were conducted, aimed at evaluating the similarity between the exact result set and the approximate ones returned by our distributed algorithm as well as the scalability of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1145/1066677.1066796 | SAC |
Keywords | Field | DocType |
frequent itemsets,approximate mining,exact result set,exact computation,effective method,available datasets,unfrequent itemsets,frequent pattern,proposed algorithm,distributed algorithm,data mining | Data mining,Result set,Effective method,Computer science,Association mining,Distributed algorithm,Computation,Scalability | Conference |
ISBN | Citations | PageRank |
1-58113-964-0 | 8 | 0.49 |
References | Authors | |
22 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Claudio Silvestri | 1 | 136 | 10.21 |
| Salvatore Orlando | 2 | 1595 | 202.29 |