Data mining, a relatively young and interdisciplinary field of computing, is the process of discovering new patterns from large data sets involving methods from statistics, artificial intelligence, and database management. The data mining task is the automatic or semi-automatic analysis of large quantities of data in order to extract previously unknown interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection) and dependencies (association rule mining). These patterns can then be seen as a kind of summary of the input data, and used in further analysis or for example in machine learning and predictive analytics.
Faculty in this area include:
YongYeol Ahn, Ariful Azad, Katy Börner, David Crandall, Mehmet Dalkilic, Ying Ding, Dennis Groth, Roni Khardon, Xiaojing Liao, Filippo Menczer, John Paolillo, Beth Plale, Paul Purdom, Predrag Radivojac, Luis Rocha, Haixu Tang, Dirk Van Gucht, Shuang Wang, David Wild, Grigory Yaroslavtsev, Yuzhen Ye, Qin Zhang