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Classification assessment based on accuracy, compactness and speed of C4.5 and CPAR : a comparative study

Rahmat, Hazwani and Mustapha, Aida and Amit, Noor Afiza and Md Said, Masniza Shaheeda Classification assessment based on accuracy, compactness and speed of C4.5 and CPAR : a comparative study. In: International Conference on Artificial Intelligence and Machine Learning (AIML-11), 12-14 April 2011, Dubai, United Arab Emirates.

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Abstract

Classification is a widely used data mining task in organizing business operations. The classification task is used to conjunction with the association rule mining task to form an associative classification. This paper investigates performance of the associative classifier C4.5. The experiments use UCI zoo dataset with different attribute dimensions in measuring the speed, accuracy, and compactness between the two classifier CPAR and C4.5. The experiments show that CPAR algorithms outperformed C4.5 The experiments show that CPAR algorithms outperformed C4.5 in all aspects measured and are at higher performance, which is consistent to previous results.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: classification; association-based classification; predictive association rules
Subjects: Q Science > QA Mathematics > QA75 Calculating machines > QA75.5 Electronic computers. Computer science
Divisions: Faculty of Computer Science and Information Technology > Department of Software Engineering
Depositing User: Normajihan Abd. Rahman
Date Deposited: 31 Jan 2013 08:38
Last Modified: 21 Jan 2015 07:52
URI: http://eprints.uthm.edu.my/id/eprint/2959
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