Abstract—Associative classification (AC) is an approach in data mining that uses association rule to build classification systems that are easy to interpret by end-user. When different data operations (adding, deleting, updating) are applied against certain training data set, the majority of current AC algorithms must scan the complete training dataset again to update the results (classifier) in order to reflect change caused by such operations. This paper deals with data insertion issue within the incremental learning in AC mining. Particularly, we modified a known AC algorithm called CBA to treat one aspect of the incremental data problem which is data insertion. The new algorithm called Associative Classification based on Incremental Mining (ACIM). Experimental results against six data sets from UCI data repository showed that the proposed incremental algorithm reduces the computational time if compared to CBA, and almost derives the same accuracy of it.
Index Terms—Associative classification, CBA, data mining, Incremental mining.
M. H. Alnababteh, M. Alfyoumi, and A. Aljumah are with the Salman Bin Abdulaziz University, Kharj, KSA (e-mail: nababteh@gmail.com, fayomi66@yahoo.com, aljumah88@hotmail.com).
J. Ababneh is with the World Islamic Sciences and Education University, Amman, Jordan (e-mail: ababnehjafar@yahoo.com, jafar.ababneh@wise.edu.jo).
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Cite:Mohammed H. Alnababteh, M. Alfyoumi, A. Aljumah, and J. Ababneh, "Associative Classification Based on Incremental Mining (ACIM)," International Journal of Computer Theory and Engineering vol. 6, no. 2, pp. 135-140, 2014.
