Review on: Techniques for Predicting Frequent Items

EOI: 10.11242/viva-tech.01.01.05

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Himanshu A. Chaudhari, Darshana S. Vartak, Nidhi U. Tripathi, Sunita Naik, "Review on: Techniques for Predicting Frequent Items", VIVA-Tech IJRI Volume 1, Issue 1, Article 5, pp. 1-8, Oct 2018. Published by Computer Engineering Department, VIVA Institute of Technology, Virar, India.


Electronic commerce(E- Commerce) is the trading or facilitation of trading in products or services using computer networks, such as the Internet. It comes under a part of Data Mining which takes large amount of data and extracts them. The paper uses the information about the techniques and methods used in the shopping cart for prediction of product that the customer wants to buy or will buy and shows the relevant products according to the cost of the product. The paper also summarizes the descriptive methods with examples. For predicting the frequent pattern of itemset, many prediction algorithms, rule mining techniques and various methods have already been designed for use of retail market. This paper examines literature analysis on several techniques for mining frequent itemsets.The survey comprises various tree formations like Partial tree, IT tree and algorithms with its advantages and its limitations.


Association Rule Mining, Data Mining, Frequent Itemsets, IT tree, Market Basket Data, Prediction.


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