Welcome to the InfoSci Platform
Strong Symmetric Association Rules and Interestingness Measures

Strong Symmetric Association Rules and Interestingness Measures

Agathe Merceron
Copyright: © 2010 |Pages: 19
ISBN13: 9781605667546|ISBN10: 1605667544|ISBN13 Softcover: 9781616924508|EISBN13: 9781605667553
DOI: 10.4018/978-1-60566-754-6.ch012
Cite Chapter Cite Chapter

MLA

Merceron, Agathe. "Strong Symmetric Association Rules and Interestingness Measures." 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. 185-203. https://doi.org/10.4018/978-1-60566-754-6.ch012

APA

Merceron, A. (2010). Strong Symmetric Association Rules and Interestingness Measures. In Y. Koh & N. Rountree (Eds.), Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection (pp. 185-203). IGI Global. https://doi.org/10.4018/978-1-60566-754-6.ch012

Chicago

Merceron, Agathe. "Strong Symmetric Association Rules and Interestingness Measures." In Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection, edited by Yun Sing Koh and Nathan Rountree, 185-203. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-754-6.ch012

Export Reference

Mendeley
Favorite

Abstract

Strong symmetric association rules are defined as follows. Strong means that the association rule has a strong support and a strong confidence, well above the minimum thresholds. Symmetric means that X?Y and Y?X are both association rules. Common objective interestingness measures such as lift, correlation, conviction or Chi-square tend to rate this kind of rule poorly. By contrast, cosine is high for such rules. However, depending on the application domain, these rules may be interesting regarding criteria such as unexpectedness or actionability. In this chapter, the authors investigate why the abovementioned measures, except cosine, rate strong symmetric association rules poorly, and show that the underlying data might take a quite special shape. This kind of rule can be qualified as rare, as they would be pruned by many objective interestingness measures. Then the authors present lift and cosine in depth, giving their intuitive meaning, their definition and typical values. Because lift has its roots in probability and cosine in geometry, these two interestingness measures give different information on the rules they rate. Furthermore they are fairly easy to interpret by domain experts, who are not necessarily data mining experts. They round off our investigation with a discussion on contrast rules and show that strong symmetric association rules give a hint to mine further rare rules, rare in the sense of a low support but a high confidence. Finally they present case studies from the field of education and discuss challenges.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.