Name
Abstract | ||
|---|---|---|
Mining frequent patterns focus on discover the set of items which were frequently purchased together, which is an important data mining task and has broad applications. However, traditional frequent pattern mining does not consider the characteristics of the customers, such that the frequent patterns for some specific customer groups cannot be found. Multidimensional frequent pattern mining can find the frequent patterns according to the characteristics of the customer. Therefore, we can promote or recommend the products to a customer according to the characteristics of the customer. However, the characteristics of the customers may be the continuous data, but frequent pattern mining only can process categorical data. This paper proposes an efficient approach for mining multidimensional frequent pattern, which combines the clustering algorithm to automatically discretize numerical-type attributes without experts. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-36546-1_6 | ACIIDS (1) |
Keywords | Field | DocType |
traditional frequent pattern mining,continuous data,relational database,specific customer group,categorical data,broad application,multidimensional frequent pattern mining,important data mining task,frequent pattern mining,frequent pattern,multidimensional frequent pattern,data mining,discretization,clustering | Data mining,Relational database,Categorical variable,Computer science,Cluster analysis | Conference |
Volume | ISSN | Citations |
7802 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 10 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yue-Shi Lee | 1 | 543 | 41.14 |
| Show-Jane Yen | 2 | 537 | 130.05 |