Name
Abstract | ||
|---|---|---|
The step of mining frequent itemsets in database is the essential step and most expensive in the process of mining association rules in data mining task, many algorithms of mining frequent itemsets have been proposed to improve the performance of Apriori Algorithm. In this paper, we have introduced an optimization in the phase of generation pruning of candidates by a new strategy for the calculation of frequent itemsets based on approximate values of supports exact the itemsets. We have evaluated our algorithm AprioriMin against three popular frequent itemsets mining algorithms - Apriori and FP-growth, Close using two data sets with a variety of minimum support. |
Year | Venue | Keywords |
|---|---|---|
2016 | 2016 4TH IEEE INTERNATIONAL COLLOQUIUM ON INFORMATION SCIENCE AND TECHNOLOGY (CIST) | Frequent itemsets, Support, Pruning of candidates, Performance |
Field | DocType | ISSN |
Data mining,Approximation algorithm,Data set,Algorithm design,Computer science,A priori and a posteriori,Apriori algorithm,Association rule learning | Conference | 2327-185X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Houda Essalmi | 1 | 0 | 0.34 |
| Mohamed El Far | 2 | 0 | 0.34 |
| El Mohajir Mohammed | 3 | 6 | 3.80 |
| Mohamed Chahhou | 4 | 4 | 0.77 |