Product Recommendation System using MLP Algorithm
Author(s) -
Prof. Sneha Jadhav,
Saurabh Kinholkar,
Akshay Kamble
Publication year - 2022
Publication title -
international journal of advanced research in science communication and technology
Language(s) - English
Resource type - Journals
ISSN - 2581-9429
DOI - 10.48175/ijarsct-3206
Subject(s) - association rule learning , computer science , affinity analysis , data mining , apriori algorithm , set (abstract data type) , product (mathematics) , domain (mathematical analysis) , knowledge extraction , order (exchange) , information retrieval , machine learning , data science , mathematics , finance , mathematical analysis , geometry , economics , programming language
DATA mining is a vital analysis domain today that focuses on data discovery in databases. It’s wherever knowledge from the database are mined in order that informative knowledge will be made and used effectively and with efficiency by humans. Prediction and description are its objectives. One amongst the aspects of information mining is that the Association Rule mining. It consists of two procedures: First, finding the frequent item set within the info employing a minimum support and constructing the association rule from the frequent item set with such confidence. It relates to the association of things whereby for each prevalence of A, there exists an occasion of B. This mining is additional applicable within the market basket analysis. Purchasers who buy certain items will benefit from that application. What is the attainable item that matches every item that they purchase? Among the most widely used association rule mining algorithms are MLPs and genetic algorithms.
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