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New applications from the areas of analytical data processing and data integration require powerful features to condense and reconcile available data. As outlined in [1], the general concept of grouping and aggregation appears to be a fitting paradigm for a number of these issues, but in its common form of equality based groups or with current extensions like simple user-defined functions to derive group-by values on a per tuple basis and restricted aggregate functions a number of problems remain unsolved. We describe two extensions to the grouping mechanism, a generic one to support holistic user-defined grouping functions and higher level construct that provides similarity based grouping suitable in a number of applications like duplicate detection and elimination.