Decision Trees (DTs) are one of the most widely used supervised Machine Learning algorithms. The algorithm constructs binary tree data structures that partition the data into smaller segments according to different rules. Hence, DTs can be used as a learning process of finding the optimal rules to separate and classify all items of a dataset. Since the algorithm relies on a decision process similar to rule-based decisions, they are easily interpretable. However, DTs can be difficult to analyse when dealing with large datasets and/or with multiple trees, i.e. ensembles. To ease the analysis and validation of these models, we developed a visual tool which includes a set of visualizations that overview and give details of a set of trees. Our tool aims to provide different perspectives over the same data and provide further insights on how decisions are being made. In this article, we overview our design process, present the different visualization models and their iterative validation. We present a use case in the telecommunications domain. In concrete, we use the visual tool to help understand how a model based on DTs decides which is the best channel (i.e., phonecall, e-mail, SMS) to contact a client.

Data is everywhere, our society shapes itself through it and, with the years passing by, it is becoming greater in dimension. A lot of this data now available is multivariate in nature. The bigger the volume and complexity are, the harder tasks to detect, classify, and measure characteristics and relations within data. Glyph-based visualization is one of the possible techniques commonly adopted in data science to address the representation of multivariate data. Multiple varieties of glyph design have been developed and studied over the past decades. However, little research was done to compare the effectiveness of glyphs designs developed for a specific context of the application. This paper aims to study data glyphs and their applications. More specifically, three visual explorations are produced as glyph design alternatives to represent a dataset related to audiological tests carried out in the population of Portugal. These glyphs were evaluated through controlled semi-structured experiments with users, and a crowdsourced experiment, and then an analysis of the performance result of each of the glyphs is presented, evaluating them in terms of learning and memorization. The results show how the use of metaphors and semantic relations to represent the attributes helps in understanding the glyph and its memorization. Additionally, we identified that the redundancy in encoding data might be beneficial.

Automatic fraud detection and prevention are challenging problems that have attracted the attention of many researchers in academia and industry. Over the last few years, many improvements have been achieved, especially in predictive models based on Machine Learning. However, a considerable amount of these models only provide a prediction score and a short explanation which may not be enough to make informed decisions. This paper presents a visualization tool that aims to assist fraud analysts in making informed decisions and increase their effectiveness in the detection of fraud. To this end, we designed three visualisation models that apply state of the art techniques to support the analysis of fraudulent transactions. To demonstrate the analytic capabilities and benefits of the proposed tool, we discussed a real use case scenario and conducted user testing with real fraud analysts. Through the feedback from both studies, we were able to conclude that the tool is an asset to facilitate the detection of suspicious events as well to improve the analysis times of the fraud analysts’ work process.

To analyse and detect fraudulent patterns in banking transactions, most fraud analysts use spreadsheets which makes the overall process time-consuming and complex. In this article, we propose a visualization tool that aims to ease the analysis of banking transactions over time and the detection of the transactions' topology and of suspicious behaviours. Our main contributions are: (i) a user-centred visual tool, developed with the aid of fraud experts; (ii) a method that characterises the transactions topology through a self-organising algorithm; (iii) the visual characterisation of transactions through complex glyphs; and (iv) a user study to assess the tool effectiveness.

Through Data Visualisation and Sonification models, we present a study of multimodal representations to characterise the Portuguese consumption patterns, which were gathered from Portuguese hypermarkets and supermarkets over the course of two years. We focus on the rhythmic nature of the data to create and discuss audio and visual representations that highlight disruptions and sudden changes in the normal consumption patterns. For this study, we present two distinct visual and audio representations and discuss their strengths and limitations.

In the analysis of time-series, it is common to focus on the identification of changing behaviours and patterns that repeat over time. In this article, we propose a visualisation model based on a radial calendar to analyse the Portuguese's consumption data. The model is intended to assist the analysts within a Portuguese Retail Company in the identification of periodic patterns and deviations from the normal consumption values. Our main contributions are: (i) the representation and characterisation of the Portuguese's consumption behaviour over time; (ii) a radial visualisation model to identify consumption patterns and their deviations; and (iii) a user case study to compare this visualisation model to a regular calendar layout. Our model has as main requirement the representation of the maximum amount of data in one single space. As such, it is ideal for analysts without prior knowledge of the data, since it provides an effective and efficient qualitative overview of the Portuguese's consumption.

We present MixMash, an interactive tool to assist users in the creation of music mashups based on cross-modal associations between musical content analysis and information visualisation. Our point of departure is a harmonic mixing method for musical mashups by Bernardes et al. [1]. To surpass design limitations identified in the previous method, we propose a new interactive visualisation of multidimensional musical attributes-hierarchical harmonic compatibility, onset density, spectral region, and timbral similarity-extracted from a large collection of audio tracks. All tracks are represented as nodes whose distances and edge connections indicate their harmonic compatibility as a result of a force-directed graph. In addition, we provide a visual language that aims to enhance the tool usability and foster creative endeavour in the search for meaningful music mixes.