Frequent Itemset Mining and Association Rules

Frequent Itemset Mining and Association Rules

Susan Imberman (City University of New York, USA) and Abdullah Uz Uz Tansel (Bilkent University, Turkey)
DOI: 10.4018/978-1-59904-933-5.ch076
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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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