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A Comparative Study of Pattern Recognition Algorithms on Sales Data

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Maulik Shah, Nirali Shah, Anviksha Shetty, Darshan Shah, Pradnya Gotmare

Anviksha Shetty Darshan Shah Maulik Shah Nirali Shah and Pradnya Gotmare. A Comparative Study of Pattern Recognition Algorithms on Sales Data. International Journal of Computer Applications 141(1):38-41, May 2016. BibTeX

	author = {Maulik Shah, Nirali Shah, Anviksha Shetty, Darshan Shah and Pradnya Gotmare},
	title = {A Comparative Study of Pattern Recognition Algorithms on Sales Data},
	journal = {International Journal of Computer Applications},
	issue_date = {May 2016},
	volume = {141},
	number = {1},
	month = {May},
	year = {2016},
	issn = {0975-8887},
	pages = {38-41},
	numpages = {4},
	url = {},
	doi = {10.5120/ijca2016909463},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


In the realm of Data Mining looking for patterns and association rules is a very critical task and has been widely studied in the past years. There exist several data mining algorithms to find Association Rules in given datasets. One of the most popular and widely used algorithm is the Apriori algorithm to find patterns and itemsets in huge datasets and getting the association rules between them. This is done to gather knowledge from otherwise unsuspecting and random data. The Fp-Growth algorithm is similarly a different algorithm which uses an extended frequent pattern prefix-tree data structure for storing critical data after compression about frequent pairs. In this paper we do a comparative analysis of the 2 most popular pattern recognition algorithms and their performance on sales data of a college canteen sales transnational database where each record consists of items purchased by customer.


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Comparison, Data Mining, Frequent, Itemset, Apriori, Algorithm, FP-Growth, Knowledge Discovery.