Researcher

Len Feremans

Research Expertise:Development and study of advanced data mining and machine learning methods. In particular, we investigate: (i) new methods to efficiently discover interesting patterns in sequential data; (ii) new methods to detect contextual anomalies in heterogeneous sequential data; (iii) and new methods for multi-label classification in extremely large datasets. In addition, we investigate applications of these methods in areas such as the monitoring of wind farms and anomaly detection in an Industrial Internet of Things context.

Keywords:DATA MINING, MACHINE LEARNING, PATTERN MINING, DATA SCIENCE, Computer science (incl. applied informatics)

Disciplines:Artificial intelligence, Computer architecture and networks, Distributed computing, Information systems, Scientific computing, Theoretical computer science, Visual computing, Signal processing, Product development, Data mining, Knowledge representation and reasoning, Machine learning and decision making, Natural language processing

Research techniques:Development of algorithmic solutions to (un)supervised machine learning problems; Formalizing research problems mathematically; Development of algorithmic solutions; Analysis of properties of algorithms; Case studies: application of developed techniques in real-life contexts.

Users of research expertise:All sectors in which data mining or machine learning is applied. More specifically, anomaly detection, prediction and/or discovering patterns in sequential data, such as event log data and time series. There are existing collaborations for: (i) predicting labels for federal police; (ii) condition monitoring in wind turbine farms (using pattern mining); (iii) anomaly detection in water consumption of supermarket chains; (iv) data cleaning and entity resolution to combine different databases.