A COMPARATIVE STUDY OF ASSOCIATION RULE MINING TECHNIQUES AND PREDICTIVE MINING APPROACHES FOR ASSOCIATION CLASSIFICATION

Kavita Mittal, Gaurav Aggarwal, Prerna Mahajan

Abstract


Abstract: ARM and classification are integrated together to build competitive classifier models called Associative Classifiers and this approach is known as Association Classification (AC). AC leads to the formation of accurate classifier consisting of significant rules capable of predicting the class of the data. This paper presents the evolution of ARM to AC highlighting the development and improvements in ARM techniques followed by AC techniques. The goal of this paper is to survey and understand different ARM and AC techniques and comparing their performance. In the literature a variety of AC algorithms have be proposed such as CBA, CMAR, MCAR, CPAR etc each adopting some or the other approach for rule learning in the initial stages. This paper also presents the importance of the rule pruning methodology with the brief survey of different methods discussed in the literature. This paper also enlightens the learning approaches adopted by different AC techniques in different domains.

Keywords


Association Rule Mining (ARM), Association Classification (AC),Rule Learning, Rule Pruning, Prediction, Class Assignment.

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DOI: https://doi.org/10.26483/ijarcs.v8i9.4984

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