The KDDE research group was formed in 2008, as a branch of the LACAM, the Knowledge acquisition and Machine Learning Lab of the Department of Computer Science.
The group's research is focused on methods and techniques for knowledge discovery in databases, data mining and data analysis, and on modern technologies of datawarehousing, business intelligence and Big Data.
Specifically, the group has gained expertise in the analysis of large volumes of structured relational and linked data, spatial and/or temporal data, data streams, semi-structured data (HTML pages, transcriptions of Roman inscriptions) and unstructured data (document images, free text in English and Italian).
The analyzed methods and techniques concern both descriptive data mining tasks (analysis of associations and clustering) and predictive data mining tasks (classification, regression, interpolation and prediction).
Spatial, temporal and relational autocorrelation and algorithmic efficiency (also on distributed architectures) are two issues recently addressed. The methods and techniques proposed and studied by the group have been applied in various areas, such as bioinformatics and life sciences, renewable energy, environmental monitoring, and automatic extraction of information from documents, web pages and epigraphic databases.
Effectiveness of methods and techniques is always experimentally verified.