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A Comparative Study on Frequent Pattern Mining Algorithms

Dr. E. Mary Shyla, Jitha Janardhanan


Frequent pattern mining has been an important subject matter in data mining from many years. Many efficient algorithms have been designed for finding frequent search patterns in transactional database .Discovering frequent itemsets is the computationally intensive step in the task of mining association rules. A large number of candidate itemsets generation is one of the main challenge in mining. The objective of frequent pattern mining is to find frequently appearing subsets in a given sequence of sets. Frequent pattern mining comes across as a sub-problem in various other fields of data mining such as association rules discovery, classification, market analysis, clustering, web mining, etc. Various methods and algorithms have been proposed for mining frequent pattern.This paper presents comparative study on frequent mining techniques – Apriori and FP-Growth. [2]


Aproiri, Fpgrowth, Hadoop, Frequent Pattern Mining

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“Frequent Pattern Mining Algorithms: A Comparative Study,”MrSahilModak,MrSagarVikmam,Prof(Mrs) Lynette D’mello, International Journal of Innovations & Advancement in Computer Science,Volume 4, Issue 9,2015

“Frequent Pattern Mining Algorithms Analysis”,RiteshGiri , Ananta Bhatt , Aadhya Bhatt , International Journal of Computer Applications, Volume 145 – No.9, July 2016

” A distributed frequent itemsetmining algorithm using Spark for Big Data analytics”, Feng Zhang, Min Liu, Weiming Shen, Article in Cluster Computing • October 2015,

” Comparing Dataset Characteristics that Favour the Apriori, Eclat or FP-Growth Frequent Itemset Mining Algorithms”, Jeff Heaton,arXiv:1701.09042v1 [cs.DB] 30 Jan 2017

” An Enhanced Frequent Pattern Growth Based On MapReduce For Mining Association Rules”, ArkanA. G. Al-Hamodi1 , Songfeng Lu, Yahya E. A. Al-Salhi, International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.6, No.2, March 2016

” A comparative study of Frequent pattern mining Algorithms: Apriori and FP Growth on Apache Hadoop”,Ahilandeeswari.G,Dr. R. ManickaChezian, International Journal of Innovations & Advancement in Computer Science, Volume 4, Special Issue ,March 2015, ISSN 2347 – 8616

M.-Y. Lin, P.-Y. Lee, and S.-C. Hsueh, “Apriori-based frequent itemset mining algorithms on mapreduce,” in Proc. 6th Int. Conf. Ubiquitous Inform. Manag. Commun., 2012, pp. 76:1–76:8.

L. Zhou, Z. Zhong, J. Chang, J. Li, J. Huang, and S. Feng, “Balanced parallel FP-growth with mapreduce,” in Proc. IEEE Youth Conf. Inform. Comput. Telecommun., 2010, pp. 243–246.


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