Evaluation and optimization of frequent association rule based classification
Deriving useful and interesting rules from a data mining system is an essential and important task. Problems such as the discovery of random and coincidental patterns or patterns with no significant values, and the generation of a large volume of rules from a database commonly occur. Works on sust...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2014
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| Online Access: | http://journalarticle.ukm.my/6804/1/4801-11319-1-PB.pdf |
| _version_ | 1825725876190314496 |
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| author | Izwan Nizal Mohd Shaharanee, Jastini Jamil, |
| author_facet | Izwan Nizal Mohd Shaharanee, Jastini Jamil, |
| author_sort | Izwan Nizal Mohd Shaharanee, |
| collection | UKM |
| description | Deriving useful and interesting rules from a data mining system is an essential and important task. Problems
such as the discovery of random and coincidental patterns or patterns with no significant values, and the
generation of a large volume of rules from a database commonly occur. Works on sustaining the interestingness
of rules generated by data mining algorithms are actively and constantly being examined and developed. In this
paper, a systematic way to evaluate the association rules discovered from frequent itemset mining algorithms,
combining common data mining and statistical interestingness measures, and outline an appropriated sequence of usage is presented. The experiments are performed using a number of real-world datasets that represent diverse characteristics of data/items, and detailed evaluation of rule sets is provided. Empirical results show that with a proper combination of data mining and statistical analysis, the framework is capable of eliminating a large number of non-significant, redundant and contradictive rules while preserving relatively valuable high accuracy and coverage rules when used in the classification problem. Moreover, the results reveal the important characteristics of mining frequent itemsets, and the impact of confidence measure for the classification task. |
| first_indexed | 2024-03-06T04:02:21Z |
| format | Article |
| id | ukm.eprints-6804 |
| institution | Universiti Kebangsaan Malaysia |
| language | English |
| last_indexed | 2024-03-06T04:02:21Z |
| publishDate | 2014 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| record_format | dspace |
| spelling | ukm.eprints-68042016-12-14T06:42:14Z http://journalarticle.ukm.my/6804/ Evaluation and optimization of frequent association rule based classification Izwan Nizal Mohd Shaharanee, Jastini Jamil, Deriving useful and interesting rules from a data mining system is an essential and important task. Problems such as the discovery of random and coincidental patterns or patterns with no significant values, and the generation of a large volume of rules from a database commonly occur. Works on sustaining the interestingness of rules generated by data mining algorithms are actively and constantly being examined and developed. In this paper, a systematic way to evaluate the association rules discovered from frequent itemset mining algorithms, combining common data mining and statistical interestingness measures, and outline an appropriated sequence of usage is presented. The experiments are performed using a number of real-world datasets that represent diverse characteristics of data/items, and detailed evaluation of rule sets is provided. Empirical results show that with a proper combination of data mining and statistical analysis, the framework is capable of eliminating a large number of non-significant, redundant and contradictive rules while preserving relatively valuable high accuracy and coverage rules when used in the classification problem. Moreover, the results reveal the important characteristics of mining frequent itemsets, and the impact of confidence measure for the classification task. Penerbit Universiti Kebangsaan Malaysia 2014-06 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/6804/1/4801-11319-1-PB.pdf Izwan Nizal Mohd Shaharanee, and Jastini Jamil, (2014) Evaluation and optimization of frequent association rule based classification. Asia-Pacific Journal of Information Technology and Multimedia, 3 (1). pp. 1-13. ISSN 2289-2192 http://ejournal.ukm.my/apjitm |
| spellingShingle | Izwan Nizal Mohd Shaharanee, Jastini Jamil, Evaluation and optimization of frequent association rule based classification |
| title | Evaluation and optimization of frequent association rule based classification |
| title_full | Evaluation and optimization of frequent association rule based classification |
| title_fullStr | Evaluation and optimization of frequent association rule based classification |
| title_full_unstemmed | Evaluation and optimization of frequent association rule based classification |
| title_short | Evaluation and optimization of frequent association rule based classification |
| title_sort | evaluation and optimization of frequent association rule based classification |
| url | http://journalarticle.ukm.my/6804/1/4801-11319-1-PB.pdf |
| work_keys_str_mv | AT izwannizalmohdshaharanee evaluationandoptimizationoffrequentassociationrulebasedclassification AT jastinijamil evaluationandoptimizationoffrequentassociationrulebasedclassification |