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A Study of Argument Acceptability Dynamics Through Core and Remainder Sets Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Discovering Overlapping Quantitative Associations by Density-Based Mining of Relevant Attributes

Authors : Thomas Van Brussel, Emmanuel Müller, Bart Goethals

Published in: Foundations of Information and Knowledge Systems

Publisher: Springer International Publishing

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Abstract

Association rule mining is an often used method to find relationships in the data and has been extensively studied in the literature. Unfortunately, most of these methods do not work well for numerical attributes. State-of-the-art quantitative association rule mining algorithms follow a common routine: (1) discretize the data and (2) mine for association rules. Unfortunately, this two-step approach can be rather inaccurate as discretization partitions the data space. This misses rules that are present in overlapping intervals.
In this paper, we explore the data for quantitative association rules hidden in overlapping regions of numeric data. Our method works without the need for a discretization step, and thus, prevents information loss in partitioning numeric attributes prior to the mining step. It exploits a statistical test for selecting relevant attributes, detects relationships of dense intervals in these attributes, and finally combines them into quantitative association rules. We evaluate our method on synthetic and real data to show its efficiency and quality improvement compared to state-of-the-art methods.

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Metadata
Title
Discovering Overlapping Quantitative Associations by Density-Based Mining of Relevant Attributes
Authors
Thomas Van Brussel
Emmanuel Müller
Bart Goethals
Copyright Year
2016
Book
Foundations of Information and Knowledge Systems

Print ISBN: 978-3-319-30023-8

Electronic ISBN: 978-3-319-30024-5

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-30024-5_8

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