A Survey on Frequent Pattern Mining Methods
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2016, Vol 4, Issue 7
Abstract
In this paper, the frequent pattern mining methods have been discussed that play very important role to generate association rules. The aim of frequent pattern mining is to search repeatedly occurring relationships in a data set. A comparative study between the algorithms from Apriori to more advanced Frequent Pattern growth approach and its variations have been done in this paper. Haoyuan Li et al (2008) suggested that PFP (Parallel Frequent Pattern) algorithm is more efficient as it parallelizes the FP (Frequent Pattern) growth approach. Again, Le Zhou et al (2010) proposed BPFP (Balanced Parallel FP) where the load is balanced to make the algorithm more efficient. The frequent patterns discovered by these algorithms are useful in other data mining applications such as classification, correlations and other relationships among data. The discovery of interesting relationships among a large amount of records can help in decision making process. This paper examines the frequent pattern algorithms, their enhancements, limitations and advantages.
Authors and Affiliations
Akansha Pandey, Shri Prakash Dwivedi, H. L. Mandoria
Keywords
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