This thesis is dedicated to the problem of comparing two soft (fuzzy/ probabilistic, possibilistic) partitions of a same set of individuals into several clusters. Its solution stands on the formal definition of concordance measures based on the principles of historical measures developped for comparing strict partitions and can be used invarious fields such as biology, image processing and clustering. Depending on whether they focus on the observation of the relations between the individuals described by each partition or on the quantization of the similarities between the clusters composing those partitions, we distinguish two main families for which the very notion of concordance between partitions differs, and we propose to characterize their representatives according to a same set of formal and informal properties. From that point of view, the measures are also qualified according to the nature of the compared partitions. A study of the multiple constructions on which the measures of the literature lie completes our taxonomy. We propose three new soft comparison measures taking benefits of the state of art. The first one is an extension of a strict approach, while the two others lie on native approaches, one individual-wise oriented, the other cluster-wise, both specifically defined to compare soft partitions. Our propositions are compared to the existing measures of the literature according to a set of experimentations chosen to cover the various issues of the problem. The given results clearly show how relevant our measures are. Finally we open new perspectives by proposing the premises of a new framework unifying most of the individual-wise oriented measures.
