A Study on Associative Classification Mining

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dc.categoryConference Proceedings
dc.contributor.authorKalpana, B
dc.date.accessioned2017-03-30T01:01:53Z
dc.date.available2017-03-30T01:01:53Z
dc.date.issued2010
dc.departmentComputer Scienceen_US
dc.description.abstractTraditional classification techniques such as ID3, C4.5, CART and RIPPER use heuristic search methods to find a small subset of patterns. In recent years, an emerging new approach in data mining that mainly uses association rules to discover patterns in classification called as associative classification which focuses on large subset of data. Algorithms like CPAR, CMAR, MCAR and MM AC are used widely in this field. Many studies show that Associative Classifiers give better accuracy than other traditional classifiers. Associative classification system is more robust and makes predictions based on entire dataset. In this paper, we aim to discuss an associative classification method by surveying and reviewing the current state-of-the-art called Incremental Learning and Mining Low Quality Data sets. This improves the overall accuracy of the associative classifier. Additionally, the classifiers produced are highly competitive with regards to error rate and efficiency.en_US
dc.identifier.urihttps://ir.avinuty.ac.in/handle/avu/2361
dc.langEnglishen_US
dc.publisher.nameProceedings of the National Conference on Advanced Computing Technologiesen_US
dc.publisher.typeNationalen_US
dc.titleA Study on Associative Classification Miningen_US
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