Work

Semantics-Based Classification of Rule Interestingness Measures

Year: 2009

Type: book-chapter

Abstract: Assessing rules with interestingness measures is the cornerstone of successful applications of association rule discovery. However, as numerous measures may be found in the literature, choosing the measures to be applied for a given application is a difficult task. In this chapter, the authors present a novel and useful classification of interestingness measures according to three criteria: the subject, the scope, and the nature of the measure. These criteria seem essential to grasp the meaning of the measures, and therefore to help the user to choose the ones (s)he wants to apply. Moreover, the classification allows one to compare the rules to closely related concepts such as similarities, implications, and equivalences. Finally, the classification shows that some interesting combinations of the criteria are not satisfied by any index.Request access from your librarian to read this chapter's full text.

Source: IGI Global eBooks

Parent work: Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction

DOI: 10.4018/978-1-60566-404-0.ch004

Authors: Julien Blanchard, Fabrice Guillet, Pascale Kuntz

Institutions: Université de Nantes, Nantes Université

Language: English

Topic: Data Mining Algorithms and Applications

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2491596913

FWCI: 0.906

Cites: 44

Cited by: 6

Related to: 10