Normalized mutual information (NMI) is a widely used measure to compare community detection methods. Recently, however, the need of adjustment for information theoretic based measures has been argued because of their tendency in choosing clustering solutions with more communities. In this paper an experimental evaluation is performed to investigate this problem, and an adjustment that scales the values of NMI is proposed. Experiments on synthetic generated networks highlight the unbiased behavior of scaled NMI.

Virtual tours in museums are an ideal solution for those that are not able to visit a museum or those who want to have a small taste of what is presented in the museum before their visit. However, these tours often encounter severe problems while users interact with them. In order to check the status of virtual tours of museums, we present the implementation of an evaluation experiment that uses a combination of two multi-criteria decision making theories, namely the analytic hierarchy process (AHP) and the fuzzy technique for order of preference by similarity to ideal solution (TOPSIS). AHP has been used for the estimation of the weights of the heuristics and fuzzy TOPSIS has been used for the evaluation of virtual tours of museums. This paper presents the exact steps that have to be followed in order to implement such an experiment and run an example experiment for virtual tours of Italian museums.