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Evaluating the correlation between objective rule interestingness measures and real human interest

Carvalho, Deborah R. and Freitas, Alex A. and Ebecken, Nelson (2005) Evaluating the correlation between objective rule interestingness measures and real human interest. In: Jorge, A. and Torgo, L. and Brazdil, P. and Camacho, R. and Gama, J., eds. Knowledge Discovery in Databases: PKDD 2005 9th European Conference on Principles and Practice of Knowledge Discovery in Databases. Lecture Notes in Computer Science . Springer, Berlin, Germany, pp. 453-461. ISBN 978-3-540-29244-9. E-ISBN 978-3-540-31665-7. (doi:10.1007/11564126_45) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1007/11564126_45

Abstract

In the last few years, the data mining community has proposed a number of objective rule interestingness measures to select the most interesting rules, out of a large set of discovered rules. However, it should be recalled that objective measures are just an estimate of the true degree of interestingness of a rule to the user, the so-called real human interest. The latter is inherently subjective. Hence, it is not clear how effective, in practice, objective measures are. More precisely, the central question investigated in this paper is: “how effective objective rule interestingness measures are, in the sense of being a good estimate of the true, subjective degree of interestingness of a rule to the user?” This question is investigated by extensive experiments with 11 objective rule interestingness measures across eight real-world data sets.

Item Type: Book section
DOI/Identification number: 10.1007/11564126_45
Uncontrolled keywords: data mining, classification rules, rule interestingness
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Sciences > School of Computing > Applied and Interdisciplinary Informatics Group
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 18:02 UTC
Last Modified: 29 May 2019 14:01 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/14243 (The current URI for this page, for reference purposes)
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