Neural Networks for Complex Data

Artificial neural networks are simple and efficient machine learning tools. Defined originally in the traditional setting of simple vector data, neural network models have evolved to address more and more difficulties of complex real world problems, ranging from time evolving data to sophisticated data structures such as graphs and functions. This paper summarizes advances on those themes from the last decade, with a focus on results obtained by members of the SAMM team of Universit\'e Paris

Non-visual overviews of complex data sets

This paper describes the design and preliminary testing of an interface to obtain overview information from complex numerical data tables non-visually, which is something that cannot be done with currently available accessibility tools for the blind and visually impaired users. A sonification technique that hides detail in the data and highlights its main features without doing any computations to the data, is combined with a graphics tablet for focus+context interactive navigation, in an interface called TableVis. Results from its evaluation suggest that this technique can deliver better scores than speech in time to answer overview questions, correctness of the answers and subjective workload

Narrative Visualization: Sharing Insights into Complex Data

This paper is a reflection on the emerging genre of narrative visualization, a creative response to the need to share complex data engagingly with the public. In it, we explain how narrative visualization offers authors the opportunity to communicate more effectively with their audience by reproducing and sharing an experience of insight similar to their own. To do so, we propose a two part model, derived from previous literature, in which insight is understood as both an experience and also the product of that experience. We then discuss how the design of narrative visualization should be informed by attempts elsewhere to track the provenance of insights and share them in a collaborative setting. Finally, we present a future direction for research that includes using EEG technology to record neurological patterns during episodes of insight experience as the basis for evaluation

Using SOMbrero for clustering and visualizing complex data

Over the years, the self-organizing map (SOM) algorithm was proven to be a powerful and convenient tool for clustering and visualizing data. While the original algorithm had been initially designed for numerical vectors, the available data in the applications became more and more complex, being frequently too rich to be described by a fixed set of numerical attributes only. This is the case, for example, when the data are described by relations between objects (individuals involved in a social network) or by measures of resemblance/dissemblance. This presentation will illustrate how the SOM algorithm can be used to cluster and visualize complex data such as graphs, categorical time series or panel data. In particular, it will focus on the use of the R package SOMbrero, which implements an online version of the relational self-organizing map, able to process any dissimilarity data. The package offers many graphical outputs and diagnostic tools, and comes with a user-friendly web graphical interface based on R-Shiny. Several examples on various real-world datasets will be given for highlighting the functionalities of the package.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech