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What is Apriori

Chapter 33
Encyclopedia of Knowledge Management, Second Edition
A level-wise algorithm for finding association rules. Apriori uses the support of an itemset to prune the search space of all itemsets. It then uses the confidence metric to find association rules.
Published in Chapter:
Frequent Itemset Mining and Association Rules
Susan Imberman (City University of New York, USA) and Abdullah Uz Uz Tansel (Bilkent University, Turkey)
Copyright: © 2011 |Pages: 11
DOI: 10.4018/978-1-59904-931-1.ch033
Abstract
With the advent of mass storage devices, databases have become larger and larger. Point-of-sale data, patient medical data, scientific data, and credit card transactions are just a few sources of the ever-increasing amounts of data. These large datasets provide a rich source of useful information. Knowledge Discovery in Databases (KDD) is a paradigm for the analysis of these large datasets. KDD uses various methods from such diverse fields as machine learning, artificial intelligence, pattern recognition, database management and design, statistics, expert systems, and data visualization.
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Chapter 9
Measuring the Attitudes of Governmental Policies and the Public Towards the COVID-19 Pandemic
An algorithm for association methods of data mining and association rule learning over relational databases.
Published in Chapter: Measuring the Attitudes of Governmental Policies and the Public Towards the COVID-19 Pandemic; From: Handbook of Research on Interdisciplinary Perspectives on the Threats and Impacts of Pandemics
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Chapter 7
Air Pollution Monitoring in Intelligent Cities Using Weighted Association Rule Mining
This is one of the most popular association rule mining techniques that was developed by Agrawal and Srikant in 1994 to find frequent itemsets and to obtain strong association rules which are inherent in the dataset.
Published in Chapter: Air Pollution Monitoring in Intelligent Cities Using Weighted Association Rule Mining; From: Developing and Monitoring Smart Environments for Intelligent Cities
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