INTERNATIONAL RESEARCH JOURNAL OF SCIENCE ENGINEERING AND TECHNOLOGY

( Online- ISSN 2454 -3195 ) New DOI : 10.32804/RJSET

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PERFORMANCE EVALUATION OF APRIORI AND FP-GROWTH ALGORITHM ON US CENSUS DATA

    2 Author(s):  SUPRIYA SINGH,MOHIT PAUL

Vol -  9, Issue- 4 ,         Page(s) : 26 - 37  (2019 ) DOI : https://doi.org/10.32804/RJSET

Abstract

It is possible to uncover previously unknown relationships and correlations between variables. Association rule is an approach that supports the analysis of historical data and identify behaviors associated with these data for future studies. Finding the frequent patterns from huge database is a great task in data mining. In this work, a comparative study is made between classical frequent pattern mining algorithms that use candidate set generation (Apriori algorithm) and improved Apriori Algorithm with the algorithm without candidate set generation (FP growth algorithm). Apriori and FP-Growth algorithm are explained here. These both algorithms are compared and analysed to find the best one in terms of time complexity and I/O transaction. Comparison is done on US Census data with variables like housing education, employment, income, and hospitality on which official planning can be done.

• Agrawal, and Ramakrishna Srikant, (1994) “Fast algorithms for mining association rules” Proc. 20th international conference very large data bases.
• Agrawal, R., Imielinski, T., and Swami, (1993) “Mining association rules between sets of items in large databases”. ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’93), Washington.
• Amanvir Kaur, Gagandeep Jagdev, (2017) “Analyzing Working of FP-Growth Algorithm for Frequent Pattern Mining” International Journal of Research Studies in Computer Science and Engineering
• Bhandari A., Gupta A., Das D., (2015) “Improvised Apriori Algorithm using frequent pattern tree for real time applications in data mining Computer Science”.
• Borgelt C. (2012) “Frequent item set mining” Wiley Interdisciplinary Reviews Data Mining & Knowledge Discovery.
•  Han,  J.,  Kamber,  M., (2006) “Data  Mining  concepts  and techniques”,  Elsevier  Inc.,  Second  Edition,  San Francisco.
• Khanali, h., and Vaziri, B. (2017) “A survey on improved Algorithms for mining association rules”. International Journal of Computer Application (IJCA).
• Sagar, B.P., and Kale S., (2017) “Efficient algorithms to find frequent itemset using data mining”. International Research Journal of Engineering and Technology (IRJET),
• Shridhar, M., and Parmar, M. 2017. “Survey on Association rule mining and its approaches”. International Journal of Computer Sciences and Engineering (IJCSE).
• Sumit Agarwal and Vinay Singal, (2017) "A Survey on Frequent pattern mining Algorithms" International Journal of Engineering Research & Technology (IJERT),.

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