Publication Date:
2018
abstract:
The huge amount of data, generated by daily-life data sources, represents a big opportunity for the development and advancement in several fields: scientific research, social life and industry. At the same time, analyzing these big repositories is a hard challenge, since the overload of information can overwhelm our capability of reading and understanding data, making finding useful pieces of information a difficult task. In this discussion we give a general overview about Knowledge Discovery in Databases as a scientific discipline that provides methodologies, techniques and tools for dealing with Big Data in order to find underlying knowledge that can be exploited in decision making processes.
Iris type:
02.01 Contributo in volume (Capitolo o Saggio)
Keywords:
Anomaly detection; Associations rules; Classification; Clustering; CRISP-DM methodology; Data mining; Knowledge discovery; Machine learning; Model evaluation; Pattern recognition
List of contributors:
Manco, Giuseppe; Ritacco, Ettore; Guarascio, Massimo
Book title:
Reference Module in Life Sciences