Unsupervised Detection of Behavioural Drifts with Dynamic Clustering and Trajectory Analysis

Real-time monitoring of human behaviours, especially in e-Health applications, has been an active area of research in the past decades. On top of IoT-based sensing environments, anomaly detection algorithms have been proposed for the early detection of abnormalities. Gradual change procedures, commonly referred to as drift anomalies, have received much less attention in the literature because they represent a much more challenging scenario than sudden temporary changes (point anomalies). In this paper, we propose, for the first time, a fully unsupervised real-time drift detection algorithm named DynAmo, which can identify drift periods as they are happening. DynAmo comprises a dynamic clustering component to capture the overall trends of monitored behaviours and a trajectory generation component, which extracts features from the densest cluster centroids. Finally, we apply an ensemble of divergence tests on sliding reference and detection windows to detect drift periods in the behavioural sequence.

Machine Learning for Visualization Recommendation Systems: Open Challenges and Future Directions

Visualization Recommendation Systems (VRS) are a novel and challenging field of study, whose aim is to automatically generate insightful visualizations from data, to support non-expert users in the process of information discovery. Despite its enormous application potential in the era of big data, progress in this area of research is being held back by several obstacles among which are the absence of standardized datasets to train recommendation algorithms, and the difficulty in defining quantitative criteria to assess the effectiveness of the generated plots. In this paper, we aim not only to summarize the state-of-the-art of VRS, but also to outline promising future research directions.

AI-based Data Preparation and Data Analytics in Healthcare: The Case of Diabetes

The Associazione Medici Diabetologi (AMD) collects and manages one of the largest worldwide-available collections of diabetic patient records, also known as the AMD database. This paper presents the initial results of an ongoing project whose focus is the application of Artificial Intelligence and Machine Learning techniques for conceptualizing, cleaning, and analyzing such an important and valuable dataset, with the goal of providing predictive insights to better support diabetologists in their diagnostic and therapeutic choices.