Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Overview of Various Frequent Item Set Mining Algorithms of Big Data

Author : Ms. Ann Sara Sajee 1 Ms. Sheryl Saji 2 Ms. Akshada Potdar 3 Mrs. Smita Dange 4

Date of Publication :7th December 2016

Abstract: Market basket analysis (MBA) is an important component of analytical system used in retail organizations. It helps in determining the placement of goods, designing sales promotions for different segments of customers so as to improve customer satisfaction .Thus, increasing the profits of the super market. The transactions can be huge for a supermarket and hence, we have used data analysis technique to get the desired results. It works on frequent item sets to mine data .The frequent item sets are mined from the market basket database (sales records) by applying the efficient algorithms which generates the association rules as output. In this paper, we have discussed how A-priori the most popular MBA algorithm for traditional data set ,is insufficient when applied for big data .We have listed and shown the working of other frequent item set mining algorithm such as PCY and SON that can be used for big data . There are various data mining tools which are available. The various tools will also be compared in this paper.

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