Name
Title | ||
|---|---|---|
An efficient data mining approach for discovering interesting knowledge from customer transactions |
Abstract | ||
|---|---|---|
Mining association rules and mining sequential patterns both are to discover customer purchasing behaviors from a transaction database, such that the quality of business decision can be improved. However, the size of the transaction database can be very large. It is very time consuming to find all the association rules and sequential patterns from a large database, and users may be only interested in some information. Moreover, the criteria of the discovered association rules and sequential patterns for the user requirements may not be the same. Many uninteresting information for the user requirements can be generated when traditional mining methods are applied. Hence, a data mining language needs to be provided such that users can query only interesting knowledge to them from a large database of customer transactions. In this paper, a data mining language is presented. From the data mining language, users can specify the interested items and the criteria of the association rules or sequential patterns to be discovered. Also, the efficient data mining techniques are proposed to extract the association rules and the sequential patterns according to the user requirements. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1016/j.eswa.2005.07.035 | Expert Syst. Appl. |
Keywords | Field | DocType |
sequential pattern,transaction database,efficient data mining technique,data mining,interesting knowledge,mining sequential pattern,association rule,mining association rule,large database,user requirement,customer transaction,traditional mining method,efficient data mining approach,data mining language,user requirements | Data science,Data mining,Data stream mining,Web mining,Computer science,Business decision mapping,Association rule learning,Purchasing,Database transaction,User requirements document | Journal |
Volume | Issue | ISSN |
30 | 4 | Expert Systems With Applications |
Citations | PageRank | References |
10 | 0.63 | 10 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Show-Jane Yen | 1 | 537 | 130.05 |
| Yue-Shi Lee | 2 | 543 | 41.14 |