Geometric mean with regularization factor for estimation of relevance weights in grouping relational data with multiple views

Grouping data with multiple views is characterized as the challenge of grouping similar data and separating the distinct data, using multiple representations with multiple sources. The approach uses the different views that have complementary data in order to obtain more robust results. Data grouping is one of the most relevant tools for data analysis and its importance is related to the faithful representation of real data, with the combined use of several views presenting greater accuracy compared to the single view. In this context, the use of different metrics to measure the similarities between the data and the use of relevance weights constitute an efficient approach. This work aims to investigate the use of a geometric mean with a regularization factor for the estimation of relevance weights in the grouping of relational data with multiple views.