Relational partitioning fuzzy clustering algorithms based on multiple dissimilarity matrices
Fuzzy sets and systems (Print)
This paper introduces fuzzy clustering algorithms that can partition objects taking into account simultaneously their relational descriptions given by multiple dissimilarity matrices. The aim is to obtain a collaborative role of the different dissimilarity matrices to get a final consensus partition. These matrices can be obtained using different sets of variables and dissimilarity functions. These algorithms are designed to furnish a partition and a prototype for each fuzzy cluster as well as
... o learn a relevance weight for each dissimilarity matrix by optimizing an adequacy criterion that measures the fit between the fuzzy clusters and their representatives. These relevance weights change at each algorithm iteration and can either be the same for all fuzzy clusters or different from one fuzzy cluster to another. Experiments with real-valued data sets from the UCI Machine Learning Repository as well as with interval-valued and histogram-valued data sets show the usefulness of the proposed fuzzy clustering algorithms. (Yves Lechevallier), firstname.lastname@example.org (Filipe M. de Melo). 1 Acknowledgments. Authors are grateful to the anonymous referees for their careful revision, valuable suggestions, and comments which improved this paper. This research was partially supported by grants from CNPq (Brazilian Agency) and from a conjoint research project FACEPE (Brazilian Agency) and INRIA (France).