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 modern technologies of business intelligence and Big Data analytics.
Specifically, the group has gained expertise in the analysis of large volumes of structured, relational and network data, spatial and/or temporal data, event data, data streams, semi-structured data (HTML pages, transcriptions of Roman inscriptions) and unstructured data (document images, free text in English and Italian).
The synthesized methods and techniques address both descriptive data mining tasks (analysis of associations and clustering) and predictive data mining tasks (classification, regression, interpolation and forecasting).
Applications include bioinformatics and life sciences, renewable energy, environmental monitoring, automatic extraction of information from documents, web pages and epigraphic databases.
The KDDE group has recently published the book entitled Data Mining Techniques in Sensor Networks, Summarization, Interpolation and Surveillance.
Authors: Appice, A., Ciampi, A., Fumarola, F., Malerba, D.
Introduces the trend cluster, a recently defined spatio-temporal pattern, and its use in summarizing, interpolating and identifying anomalies in sensor networks.
KDDE presentations have to be based on this template.
Group members and students who are taking a degree, are invited to use it.