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Survey on the Techniques of FP-Growth Tree for Efficient Frequent Item-set Mining

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Rana Krupali, Dweepna Garg

Rana Krupali and Dweepna Garg. Survey on the Techniques of FP-Growth Tree for Efficient Frequent Item-set Mining. International Journal of Computer Applications 160(1):39-43, February 2017. BibTeX

	author = {Rana Krupali and Dweepna Garg},
	title = {Survey on the Techniques of FP-Growth Tree for Efficient Frequent Item-set Mining},
	journal = {International Journal of Computer Applications},
	issue_date = {February 2017},
	volume = {160},
	number = {1},
	month = {Feb},
	year = {2017},
	issn = {0975-8887},
	pages = {39-43},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2017912958},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Analysis has been carried out in terms of FP-Growth Tree techniques to determine which technique can be used efficiently in order to achieve higher scalability and performance. Construction and development of classifier that works with more accuracy and performs efficiently for large database is one of the key tasks of data mining techniques. Secondly training dataset repeatedly produces massive amount of rules. It’s very tough to store, retrieve, prune, and sort a huge number of rules proficiently before applying to a classifier. In such situation FP is the best choice but problem with this approach is that it generates redundant FP Tree. A Frequent pattern tree (FP-tree) is type of prefix tree that allows the detection of recurrent (frequent) item set exclusive of the candidate item set generation. It is anticipated to recuperate the flaw of existing mining methods. FP – Trees pursues the divide and conquers tactic.


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Data Mining, KDD, Association Rule, FP-Growth Tree, FP-Growth Tree Techniques.