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Recommender systems help users to find information that best fits their preferences and needs in an overloaded search space. Most of recommender systems research focuses on improving recommendation methods to obtain a higher accuracy in recommendations. However, the study of user's inconsistencies, so-called natural noise, is becoming a hot topic in Recommender Systems. In this contribution is proposed...
In this paper, we introduce a new hierarchical agglomerative clustering process in the setting of weak orders. This process is based on consensus measures induced by weighted Kemeny distances that associate a number between 0 and 1 to each subset of weak orders. Then, clusters are sequentially formed according to the consensus among the corresponding weak orders. The process is illustrated with the...
Subgroup Discovery (SD) is a data mining technique whose main objective is the search for descriptions of subgroups of data that are statistically unusual with respect to a property of interest. General rules describing as many instances as possible are preferred in SD, but this can lead to less accurate descriptions that incorrectly describe some instances. These negative examples can be grouped...
This paper presents a summarized characterization of embedded type-1 fuzzy sets (ET1FS) by using the classical concept of convex combination given by Zadeh. ET1FSs are important when defining the centroid of an interval-valued fuzzy set (IVFS) and some type-reduction methods proposed in the literature. We will show that any ET1FS of an IVFS, with no assumption whatsoever about the universal set (either...
This paper uses the concepts of fuzzy membership and granularity proposed by Zadeh to propose a fuzzy meta-clustering algorithm for creating associated profiles of networked granules. The proposed algorithm uses repeated applications of fuzzy c-means algorithm to create soft clustering. Representation of a granule is recursively updated using the fuzzy cluster memberships of other connected granules...
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