Goal-oriented sequential pattern for network banking churn analysis

Autor:	Shao-Lun Lee, Cheng-Jung Lin, Ding-An Chiang, Yi-Fan Wang
Rok vydání:	2003
Předmět:	Association rule learning Goal orientation Computer science business.industry General Engineering Machine learning computer.software_genre Computer Science Applications Task (project management) Artificial Intelligence Artificial intelligence Data mining business computer
Zdroj:	Expert Systems with Applications. 25:293-302
ISSN:	0957-4174
DOI:	10.1016/s0957-4174(03)00073-3
Popis:	Discovering sequential patterns is one of the most important task in data mining. In this paper we propose an efficient algorithm, called Goal-oriented sequential pattern. It can provide enterprises warning signs soon before they are losing valuable customers and give them reference for decision making. Experiments comparing Apriori showed that Goal-oriented is more efficient, and performs reasonably well for the rules.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::1fb94d8e68763f86016b4c2a3b54696a https://doi.org/10.1016/s0957-4174(03)00073-3 Zobrazit plný text záznamu Full Text from ScienceDirect