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Rare Association Rule Mining: An Overview

Rare Association Rule Mining: An Overview

Yun Sing Koh, Nathan Rountree
Copyright: © 2010 |Pages: 14
ISBN13: 9781605667546|ISBN10: 1605667544|ISBN13 Softcover: 9781616924508|EISBN13: 9781605667553
DOI: 10.4018/978-1-60566-754-6.ch001
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MLA

Koh, Yun Sing, and Nathan Rountree. "Rare Association Rule Mining: An Overview." Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection, edited by Yun Sing Koh and Nathan Rountree, IGI Global, 2010, pp. 1-14. https://doi.org/10.4018/978-1-60566-754-6.ch001

APA

Koh, Y. S. & Rountree, N. (2010). Rare Association Rule Mining: An Overview. In Y. Koh & N. Rountree (Eds.), Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection (pp. 1-14). IGI Global. https://doi.org/10.4018/978-1-60566-754-6.ch001

Chicago

Koh, Yun Sing, and Nathan Rountree. "Rare Association Rule Mining: An Overview." In Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection, edited by Yun Sing Koh and Nathan Rountree, 1-14. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-754-6.ch001

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Abstract

The notion of finding rare association rules is like finding precious gems in an open field; it is a daunting task but, if successful, it is very rewarding. Association rule mining systems, such as Apriori, generally employ an exhaustive search algorithm. While these algorithms are in theory capable of finding rare association rules, they become intractable if the minimum level of support is set low enough to find rare rules. Such algorithms are therefore inadequate for finding rare associations, and also suffer from the rare item problem. Research to solve this problem has become more prevalent in recent times. The main goal of rare association rule mining is to discover relationships among sets of items in a transactional database that occur infrequently. This chapter presents a survey on the current trends and approaches in the area of rare association rule mining.

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