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Rate-Distortion Theory for Clustering in the Perceptual Space

How to extract relevant information from large data sets has become a main challenge in data visualization. Clustering techniques that classify data into groups according to similarity metrics are a suitable strategy to tackle this problem. Generally, these techniques are applied in the data space as an independent step previous to visualization. In this paper, we propose clustering on the perceptual space by maximizing the mutual information between the original data and the final visualization. With this purpose, we present a new information-theoretic framework based on the rate-distortion theory that allows us to achieve a maximally compressed data with a minimal signal distortion. Using this framework, we propose a methodology to design a visualization process that minimizes the information loss during the clustering process. Three application examples of the proposed methodology in different visualization techniques such as scatterplot, parallel coordinates, and summary trees are presented

This work has been funded in part by grants from the Spanish Government (Nr. TIN2016- 75866-C3-3-R) and from the Catalan Government (Nr. 2014-SGR-1232)

Entropy, 2017, vol. 19, n煤m. 9, p. 438

MDPI (Multidisciplinary Digital Publishing Institute)

Author: Bardera i Reig, Antoni
Bramon Feixas, Roger
Ruiz Altisent, Marc
Boada, Imma
Date: 2017 August 23
Abstract: How to extract relevant information from large data sets has become a main challenge in data visualization. Clustering techniques that classify data into groups according to similarity metrics are a suitable strategy to tackle this problem. Generally, these techniques are applied in the data space as an independent step previous to visualization. In this paper, we propose clustering on the perceptual space by maximizing the mutual information between the original data and the final visualization. With this purpose, we present a new information-theoretic framework based on the rate-distortion theory that allows us to achieve a maximally compressed data with a minimal signal distortion. Using this framework, we propose a methodology to design a visualization process that minimizes the information loss during the clustering process. Three application examples of the proposed methodology in different visualization techniques such as scatterplot, parallel coordinates, and summary trees are presented
This work has been funded in part by grants from the Spanish Government (Nr. TIN2016- 75866-C3-3-R) and from the Catalan Government (Nr. 2014-SGR-1232)
Format: application/pdf
Citation: https://doi.org/10.3390/e19090438
ISSN: 1099-4300
Document access: http://hdl.handle.net/10256/14367
Language: eng
Publisher: MDPI (Multidisciplinary Digital Publishing Institute)
Collection: MINECO/PE 2016-2019/TIN2016- 75866-C3-3-R
Reproducci贸 digital del document publicat a: https://doi.org/10.3390/e19090438
Articles publicats (D-IMA)
Is part of: Entropy, 2017, vol. 19, n煤m. 9, p. 438
Rights: Attribution 4.0 Spain
Rights URI: http://creativecommons.org/licenses/by/4.0/es/
Subject: Visualitzaci贸 de la informaci贸
Information visualization
Informaci贸, Teoria de la
Information theory
Title: Rate-Distortion Theory for Clustering in the Perceptual Space
Type: info:eu-repo/semantics/article
Repository: DUGiDocs

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