A Survey of Association Rule Classification Algorithms in Data Mining
Ramesh R1, Saravanan V2
1Ramesh R, Department of Computer Science, Sri Krishna College of Arts and Science (Tamil Nadu), India.
2Saravanan V, Department of Computer Applications, Sri Venkateswara College of Computer Applications and Management. (Tamil Nadu), India.
Manuscript received on 12 April 2019 | Revised Manuscript received on 17 May 2019 | Manuscript published on 30 May 2019 | PP: 101-107 | Volume-8 Issue-1, May 2019 | Retrieval Number: A2964058119/19©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Data mining plays a vital role in discovering hidden patterns and unknown knowledge from different types of data bases. Association rule mining is not finding specific classes instead it identifies the frequent items but in classification, classifiers are used to determine specific classes. Integrating these two techniques gives more efficient approach called Associative Classification. It is a new era in data mining approaches which is integrating Association rule and Classification to build accurate classifier than traditional methods. Most of the researchers proved that AC produces accurate results and also time efficient with different datasets. There are several algorithms proposed in recent times for associative classification (AC) such as Classification based on Association (CBA), Classification based on Multiple Association Rules (CMAR) and Classification based Predictive Association Rule (CPAR). This study compares and analyses the various important AC algorithms in terms of method, contributions, experimental results, accuracy and execution time irrespective of data sets.
Keywords: Data Mining Association Rule Classification.
Scope of the Article: Classification.