Interestingness of association rules using symmetrical tau and logistic regression
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...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Book Chapter |
| Published: |
Springer
2009
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/23232 |
| _version_ | 1848751093462335488 |
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| author | Mohd Shaharanee, Izwan Hadzic, Fedja Dillon, Tharam S |
| author2 | Ann Nicholson |
| author_facet | Ann Nicholson Mohd Shaharanee, Izwan Hadzic, Fedja Dillon, Tharam S |
| author_sort | Mohd Shaharanee, Izwan |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T07:47:15Z |
| format | Book Chapter |
| id | curtin-20.500.11937-23232 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:47:15Z |
| publishDate | 2009 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-232322022-12-09T06:09:40Z Interestingness of association rules using symmetrical tau and logistic regression Mohd Shaharanee, Izwan Hadzic, Fedja Dillon, Tharam S Ann Nicholson Xiaodong Li data mining statistical analysis interesting rules 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. 2009 Book Chapter http://hdl.handle.net/20.500.11937/23232 10.1007/978-3-642-10439-8_43 Springer restricted |
| spellingShingle | data mining statistical analysis interesting rules Mohd Shaharanee, Izwan Hadzic, Fedja Dillon, Tharam S Interestingness of association rules using symmetrical tau and logistic regression |
| title | Interestingness of association rules using symmetrical tau and logistic regression |
| title_full | Interestingness of association rules using symmetrical tau and logistic regression |
| title_fullStr | Interestingness of association rules using symmetrical tau and logistic regression |
| title_full_unstemmed | Interestingness of association rules using symmetrical tau and logistic regression |
| title_short | Interestingness of association rules using symmetrical tau and logistic regression |
| title_sort | interestingness of association rules using symmetrical tau and logistic regression |
| topic | data mining statistical analysis interesting rules |
| url | http://hdl.handle.net/20.500.11937/23232 |