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2020 | OriginalPaper | Buchkapitel

Expected vs. Unexpected: Selecting Right Measures of Interestingness

verfasst von : Rahul Sharma, Minakshi Kaushik, Sijo Arakkal Peious, Sadok Ben Yahia, Dirk Draheim

Erschienen in: Big Data Analytics and Knowledge Discovery

Verlag: Springer International Publishing

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Abstract

Measuring interestingness in between data items is one of the key steps in association rule mining. To assess interestingness, after the introduction of the classical measures (support, confidence and lift), over 40 different measures have been published in the literature. Out of the large variety of proposed measures, it is very difficult to select the appropriate measures in a concrete decision support scenario. In this paper, based on the diversity of measures proposed to date, we conduct a preliminary study to identify the most typical and useful roles of the measures of interestingness. The research on selecting useful measures of interestingness according to their roles will not only help to decide on optimal measures of interestingness, but can also be a key factor in proposing new measures of interestingness in association rule mining.

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Titel: Expected vs. Unexpected: Selecting Right Measures of Interestingness
verfasst von: Rahul Sharma
Minakshi Kaushik
Sijo Arakkal Peious
Sadok Ben Yahia
Dirk Draheim
Verlag: Springer International Publishing
Buch: Big Data Analytics and Knowledge Discovery
Print ISBN: 978-3-030-59064-2

Electronic ISBN: 978-3-030-59065-9

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-59065-9_4

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