Significance of Patterns in Data Visualisations

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http://hdl.handle.net/10138/306071

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Savvides , R , Henelius , A , Oikarinen , E & Puolamäki , K 2019 , Significance of Patterns in Data Visualisations . in KDD'19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . ACM , New York, NY , pp. 1509-1517 , ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , Anchorage , United States , 04/08/2019 . https://doi.org/10.1145/3292500.3330994

Title: Significance of Patterns in Data Visualisations
Author: Savvides, Rafael; Henelius, Andreas; Oikarinen, Emilia; Puolamäki, Kai
Other contributor: University of Helsinki, Department of Computer Science
University of Helsinki, Department of Computer Science
University of Helsinki, Department of Computer Science
University of Helsinki, Department of Computer Science

Publisher: ACM
Date: 2019
Language: eng
Number of pages: 9
Belongs to series: KDD'19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
ISBN: 978-1-4503-6201-6
DOI: https://doi.org/10.1145/3292500.3330994
URI: http://hdl.handle.net/10138/306071
Abstract: In this paper we consider the following important problem: when we explore data visually and observe patterns, how can we determine their statistical significance? Patterns observed in exploratory analysis are traditionally met with scepticism, since the hypotheses are formulated while viewing the data, rather than before doing so. In contrast to this belief, we show that it is, in fact, possible to evaluate the significance of patterns also during exploratory analysis, and that the knowledge of the analyst can be leveraged to improve statistical power by reducing the amount of simultaneous comparisons. We develop a principled framework for determining the statistical significance of visually observed patterns. Furthermore, we show how the significance of visual patterns observed during iterative data exploration can be determined. We perform an empirical investigation on real and synthetic tabular data and time series, using different test statistics and methods for generating surrogate data. We conclude that the proposed framework allows determining the significance of visual patterns during exploratory analysis.
Subject: exploratory data analysis
significance testing
visual analytics
113 Computer and information sciences
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