Medoid based semi-supervised fuzzy clustering algorithms for multi-view relational data

Abstract

In this paper we present two novel families of semi-supervised multi-view clustering algorithms for relational data, providing relevance weights for views and cluster representatives. The families differ by their cluster representative approach: set-medoids and vector set-medoids. Each family possesses four models, referred to as variants, differing by their weighting approach and the constraints imposed on those weights. Semi-supervision is attained through the use of must-link and cannot-link pairwise constraints. The algorithms perform the clustering task through the minimisation of an objective function; such minimisation is achieved through the iteration of three steps until convergence, namely: search for the best representatives, computation of the best relevance weights, and computation of the best fuzzy partition. Experiments performed on several real datasets show that those algorithms have greater robustness than other semi-supervised multi-view fuzzy clustering algorithms when handling the pairwise constraints and provide better interpretability of the results. Moreover, those experiments reveal that one family may perform better than the other depending on the data being considered; this can also be observed for the variants within each family. Furthermore, the proposed algorithms were tested in a real multi-view dataset where a synthetic noise view was added; it was observed that those algorithms managed to maintain a good level of quality in the resulting partition as well as discriminating the noise view by assigning it a lower relevance weight.