A new interestingness measure for maintaining specific attributes in Numerical Association Rule Mining

In Numerical Association Rule Mining, some attributes are more inclined to disappear. These attributes are inclined to disappear primarily when rules consist of a small value of support or confidence. These kinds of attributes are mainly discrete. In this paper, the problem of disappearing attributes is tackled by proposing a new interest measure that controls the specific qualities. The series of experiments underpins the rationale of the latest interest measure.

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Published
2025-08-07
DOI
https://doi.org/10.1109/acdsa65407.2025.11166644
Citations
1
Primary Topic
Data Mining Algorithms and Applications
Type
article
Field-Weighted Citation Impact
3.25
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A new interestingness measure for maintaining specific attributes in Numerical Association Rule Mining

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A new interestingness measure for maintaining specific attributes in Numerical Association Rule Mining

Andrés Iglesias, Damijan Novak, Domen Verber, Iztok Fister, Akemi Gavez
article en
1 citations

Abstract

In Numerical Association Rule Mining, some attributes are more inclined to disappear. These attributes are inclined to disappear primarily when rules consist of a small value of support or confidence. These kinds of attributes are mainly discrete. In this paper, the problem of disappearing attributes is tackled by proposing a new interest measure that controls the specific qualities. The series of experiments underpins the rationale of the latest interest measure.

Universidad de Cantabria (ES), University of Maribor (SI)
Openalex Percentile: Top 6%
Data Mining Algorithms and Applications
3.25
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