Before revision:  Here, we develop a fair clustering variant where the input data is a hypergraph with multiple edge types , representing information about past experiences of groups of individuals . Our method is based on diversity of experience, instead ofprotected attributes , with a goal of forming groups that have both experience and diversity with respect to participation in edge types.
After revision:  Here, we develop a fair clustering variant where the input data is a hypergraph with multiple edge types , representing information about past experiences of groups of individuals -- and the output is groups of nodes. In contrast to related problems on fair or balanced clustering, we model diversity in terms of variety of past experience (instead of, e.g., protected attributes) , with a goal of forming groups that have both experience and diversity with respect to participation in edge types.