Next Article in Journal
From Relativistic Mechanics towards Relativistic Statistical Mechanics
Next Article in Special Issue
IBVis: Interactive Visual Analytics for Information Bottleneck Based Trajectory Clustering
Previous Article in Journal
A Brief History of Long Memory: Hurst, Mandelbrot and the Road to ARFIMA, 1951–1980
Open AccessFeature PaperArticle

Rate-Distortion Theory for Clustering in the Perceptual Space

Graphics and Imaging Laboratory, University of Girona, 17003 Girona, Spain
Author to whom correspondence should be addressed.
Entropy 2017, 19(9), 438;
Received: 7 July 2017 / Revised: 28 July 2017 / Accepted: 16 August 2017 / Published: 23 August 2017
(This article belongs to the Special Issue Information Theory Application in Visualization)
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. View Full-Text
Keywords: information visualization; rate-distortion theory; clustering; information theory information visualization; rate-distortion theory; clustering; information theory
Show Figures

Graphical abstract

MDPI and ACS Style

Bardera, A.; Bramon, R.; Ruiz, M.; Boada, I. Rate-Distortion Theory for Clustering in the Perceptual Space. Entropy 2017, 19, 438.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop