Applications and Research Problems of Subgroup Mining

Knowledge Discovery in Databases (KDD) is a data analysis process which, in contrast to conventional data analysis, automatically generates and evaluates very many hypotheses, deals with complex, i.e. large, high dimensional, multi relational, dynamic, or heterogeneous data, and produces understandable results for those who "own the data". With these objectives, subgroup mining searches for hypotheses that can be supported or confirmed by the given data and that are represented as a specialization of one of three general hypothesis types: deviating subgroups, associations between two subgroups, and partially ordered sets of subgroups where the partial ordering usually relates to

• Type: Conference
• Year: 1999
• Where: ISMIS
• Authors: Willi Klösgen
• Keywords: Artificial Intelligence, Conventional Data Analysis, Data Analysis, Data Analysis Process, ISMIS 1999