Decision making based on data-analysis methods

This technical report is based on four our recent articles:"Data fusion of pre-election gallups and polls for improved support estimates", "Analyzing parliamentary elections based on voting advice application data", "The Finnish car rejection reasons shown in an interactive SOM visualization tool", and "Network visualization of car inspection data using graph layout". Neural methods are applied in political and technical decision making. We introduce decision support schemes based on Self-Organizing Map (SOM) combined with other methods. Visualizations based on various data-analysis methods are developed. In political decision making we have examples from one parliamentary election and one presidential election utilizing opinion data collected beforehand. In technical decision making we concentrate on rejection reasons in car inspection data. This technical report is a summary of our recent non-nuclear studies

The self-organizing map as a visual neighbor retrieval method

We have recently introduced rigorous goodness criteria for information visualization by posing it as a visual neighbor retrieval problem, where the task is to find proximate high-dimensional data based only on a low-dimensional display. Standard information retrieval criteria such as precision and recall can then be used for information visualization. We introduced an algorithm, Neighbor Retrieval Visualizer (NeRV), to optimize the total cost of retrieval errors. NeRV was shown to outperform alternative methods, but the SOM was not included in the comparison. In empirical experiments of this paper the SOM turns out to be comparable to the best methods in terms of (smoothed) precision but not on recall. On a related measure called trustworthiness, the SOM outperforms all others. Finally, we suggest that for information visualization tasks the free parameters of the SOM could be optimized for information visualization with cross-validation

Exploratory data analysis using self-organising maps defined in up to three dimensions

The SOM is an artificial neural network based on an unsupervised learning process that performs a nonlinear mapping of high dimensional input data onto an ordered and structured array of nodes, designated as the SOM output space. Being simultaneously a quantization algorithm and a projection algorithm, the SOM is able to summarize and map the data, allowing its visualization. Because using the most common visualization methods it is very difficult or even impossible to visualize the SOM defined with more than two dimensions, the SOM output space is generally a regular two dimensional grid of nodes. However, there are no theoretical problems in generating SOMs with higher dimensional output spaces. In this thesis we present evidence that the SOM output space defined in up to three dimensions can be used successfully for the exploratory analysis of spatial data, two-way data and three-way data. Although the differences between the methods that are proposed to visualize each group of data, the approach adopted is commonly based in the projection of colour codes, which are obtained from the output space of 3D SOMs, in some specific bi-dimensional surface, where data can be represented according to its own characteristics. This approach is, in some cases, also complemented with the simultaneous use of SOMs defined in one and two dimensions, so that patterns in data can be properly revealed. The results obtained by using this visualization strategy indicates not only the benefits of using the SOM defined in up to three dimensions but also shows the relevance of the combined and simultaneous use of different models of the SOM in exploratory data analysis

Probabilistic segmentation of volume data for visualization using SOM-PNN classifier

We present a new probabilistic classifier, called SOM-PNN classifier, for volume data classification and visualization. The new classifier produces probabilistic classification with Bayesian confidence measure which is highly desirable in volume rendering. Based on the SOM map trained with a large training data set, our SOM-PNN classifier performs the probabilistic classification using the PNN algorithm. This combined use of SOM and PNN overcomes the shortcomings of the parametric methods, the nonparametric methods, and the SOM method. The proposed SOM-PNN classifier has been used to segment the CT sloth data and the 20 human MRI brain volumes resulting in much more informative 3D rendering with more details and less artifacts than other methods. Numerical comparisons demonstrate that the SOM-PNN classifier is a fast, accurate and probabilistic classifier for volume rendering.published_or_final_versio

Beyond shape – An exploration in alternative forms for data visualization

This thesis explores the topic of alternative forms in data visualization and the ways visualization affects the communication of data it is based on. It does this through the creation of a machine learning based data visualization system prototype. It examines norms and ideals of data visualization as a set of systems aimed for simplification, situating visualization as a tool with the potential power to affect how we perceive the complexity of the world by either highlighting or obscuring information. It aims to critically highlight these norms by taking an exploratory aim to visualizing information by increasing potential interpretations of a particular set of data instead of reducing them. Norms prevalent in the field of data visualization are explored, and through this, the concept of alternative is defined. Then the dataset to visualize is defined through an exploration of current discussions around issues of increasing amounts of data, the complexity of the systems producing that data and the interpretations they enforce through the data they produce. Through this, the concept of machine detected human emotions in a text is chosen as a particular example of computational reduction to be explored through the prototype. In order to counteract this identified reduction in complexity, a system which produces a mapping between visual attributes and detected emotional attributes is proposed. The design of this system utilizes recognized critical design concepts by creating a type of post-optimal object: A visualization that causes more interpretations in its reader than reading the data itself. The process of visualization follows prevalent norms within the field but applies identified forms of alternativeness in order to create ambiguity in the visual artifacts created by the prototype. Machine learning methods are applied through a collaborative process in order to create an artificially intelligent system that automatically analyses the emotional values of a given text, and maps those to a particular set of figures. Some of the visual artifacts are then tested on a set of users, in order to assess how the visualization might affect the communication of the data it is based on and how it succeeds in increasing interpretational complexity. While not aimed toward conclusive evidence, the result of the test seems to indicate success in increasing interpretational complexity, but a lack of success in communicating the numeric data the visualizations are based on – in this sense leading to the end-result no longer being a functional data visualization, but rather a form of data-driven illustration.Denna avhandling handlar om alternativa former inom datavisualisering och sätten visualisering påverkar kommunicering av data den byggs utav, genom skapandet av en maskininlärningsbaserad datavisualiseringsprototyp. Genom det, undersöks de ideala normerna inom datavisualisering som fält som en samling konventioner med simplifiering som ändamål. Datavisualisering placeras som ett verktyg med förmågan att ändra hur vi uppfattar världens komplexitet genom att antingen framhäva eller undangömma. Genom att ställa ett explorativt mål – att visualisera data genom att utvidga tolkningar istället för att reducera dem då produceringen av den data som visualiseras är komplext är avsikten att kritiskt examinera dessa normer. Först undersöks fältets normer och genom detta definieras vad kunde anses som alternativ datavisualisering. Sedan identifieras ett komplext problem som kunde visualiseras genom en utforskning av aktuella synpunkter runt den växande mängden data I världen omkring oss och komplexiteten av de system som producerar detta data. Genom detta väljs maskinbaserad detektion av människokänslor som ett problem där maskinbaserad reduktion kan forskas genom visualisering. För att motverka reduktionistisk behandling av komplicerade domän, föreslås ett system som producerar översättningar mellan emotionella egenskaper och visuella egenskaper. Konstruktionen av detta system använder sig av kritiska designmetoder genom att bygga ett postoptimalt objekt: En datavisualisering som inte försöker kommunicera data den består utav så klart som möjligt, men istället försöker orsaka en ökande mängd tolkningar i sin läsare. Processen följer de normer som är rådande I fältet, men med ändamålet att orsaka tvetydighet för läsaren. Maskininlärning används för att implementera en kollaborativt framställd översättningsmodell mellan de emotionella och de visuella egenskaperna. Slutligen testas systemet genom en mätning av effekterna på läsare och på så sätt utvärderas visualiseringens förmåga att öka mängden tolkningar. Undersökningen har inte som mål att ge ett slutligt resultat för funktionaliteten av systemet, men skall fungera som guide för nästa iterationer. Undersökningen verkar visa att de producerade visualiseringarna lyckas i att öka mängden tolkningar för en bild till en nivå som påminner om tolkningarna för text, men lyckas inte att kommunicera känslorna från den lästa texten. Detta gör slutresultatet mer av en data-inspirerad illustration, än en datavisualisering som termen konventionellt används

Optimizing an Organized Modularity Measure for Topographic Graph Clustering: a Deterministic Annealing Approach

This paper proposes an organized generalization of Newman and Girvan's modularity measure for graph clustering. Optimized via a deterministic annealing scheme, this measure produces topologically ordered graph clusterings that lead to faithful and readable graph representations based on clustering induced graphs. Topographic graph clustering provides an alternative to more classical solutions in which a standard graph clustering method is applied to build a simpler graph that is then represented with a graph layout algorithm. A comparative study on four real world graphs ranging from 34 to 1 133 vertices shows the interest of the proposed approach with respect to classical solutions and to self-organizing maps for graphs