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Publication Number

1503017

 

Page Numbers

353-360

Paper Details

Evaluation of Faculty Performance Using Improved Apriori and Association Rule Mining

Authors

D.K. Kirange, Shubhangi D. Patil, Kanchan S. Bhagat

Abstract

Apriori is the most popular algorithm that is used to extract frequent itemsets from large data sets where these frequent itemsets can be used to generate association rules. Such rules are used as a basis for discovering knowledge such as detecting unknown relationships and producing results which can be used for decision making and prediction. When the data size is very large, both memory use and computational cost for Apriori algorithm are very expensive. And in this case the Apriori algorithm performance inefficient. In our research we propose an Adaptive Apriori approach with enhanced speedup and performance. In the proposed algorithm, the intermediate dynamic dataset is created separately using MATLAB by using the database transactions at each level separately. Thus instead of scanning the entire database, we need to scan only the extracted rows and columns at each level.

Keywords

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Citation

Evaluation of Faculty Performance Using Improved Apriori and Association Rule Mining. D.K. Kirange, Shubhangi D. Patil, Kanchan S. Bhagat. 2015. IJIRCT, Volume 1, Issue 3. Pages 353-360. https://www.ijirct.org/viewPaper.php?paperId=1503017

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