Interestingness of association rules using symmetrical tau and logistic regression
dc.contributor.author | Mohd Shaharanee, Izwan | |
dc.contributor.author | Hadzic, Fedja | |
dc.contributor.author | Dillon, Tharam S | |
dc.contributor.editor | Ann Nicholson | |
dc.contributor.editor | Xiaodong Li | |
dc.date.accessioned | 2017-01-30T12:36:07Z | |
dc.date.available | 2017-01-30T12:36:07Z | |
dc.date.created | 2010-03-30T20:02:21Z | |
dc.date.issued | 2009 | |
dc.identifier.citation | Mohd Shaharanee, Izwan and Hadzic, Fedja and Dillon, Tharam S. 2009. Interestingness of association rules using symmetrical tau and logistic regression, in Ann Nicholson and Xiaodong Li (ed), AI 2009: Advances in artificial intelligence. pp. 422-431. Heidelberg: Springer. | |
dc.identifier.uri | http://hdl.handle.net/20.500.11937/23232 | |
dc.identifier.doi | 10.1007/978-3-642-10439-8_43 | |
dc.description.abstract |
While association rule mining is one of the most popular data mining techniques, it usually results in many rules, some of which are not considered as interesting or significant for the application at hand. In this paper, we conduct a systematic approach to ascertain the discovered rules and provide a rigorous statistical approach supporting this framework. The strategy proposed combines data mining and statistical measurement techniques, including redundancy analysis, sampling and multivariate statistical analysis, to discard the non significant rules. A real world dataset is used to demonstrate how the proposed unified framework can discard many of the redundant or non significant rules and still preserve high accuracy of the rule set as a whole. | |
dc.publisher | Springer | |
dc.subject | data mining | |
dc.subject | statistical analysis | |
dc.subject | interesting rules | |
dc.title | Interestingness of association rules using symmetrical tau and logistic regression | |
dc.type | Book Chapter | |
dcterms.source.startPage | 422 | |
dcterms.source.endPage | 431 | |
dcterms.source.title | AI 2009: Advances in artificial intelligence | |
dcterms.source.isbn | 9783642104381 | |
dcterms.source.place | Heidelberg | |
dcterms.source.chapter | 68 | |
curtin.note |
The original publication is available at : | |
curtin.department | Digital Ecosystems and Business Intelligence Institute (DEBII) | |
curtin.accessStatus | Fulltext not available | |
curtin.faculty | Curtin Business School | |
curtin.faculty | The Digital Ecosystems and Business Intelligence Institute (DEBII) |