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In this paper, a novel approach is introduced to let users extract knowledge from a raw dataset in an intuitive way and using their own vocabulary. The inner structure of a raw data set is first identified using a clustering algorithm, structure on which specificity-driven measures are defined to extract the most informative knowledge. To let domain experts interact with the cluster-based structure...
We extend our previously proposed new structure for fuzzy cognitive maps (FCM) using the weighted power mean (WPM) operator to the domain of Computing With Words (CWW), where all node activations and link strengths are described using interval type-2 fuzzy membership functions which represent words. This FCM structure is both intuitive and easier to specify than crisp FCM structures, as it allows...
We describe a computationally feasible approach to perceptual computing in large social networks, where the attribute memberships and relationship strengths are described using words, and the words are represented by interval type-2 fuzzy membership functions. By employing pre-computation and storage, we reduce the queries of such networks to simple arithmetic calculations that can be feasibly performed...
Web 2.0 has provided for a rapid growth of computer mediated social networks. We first discuss how to enrich the domain of social network modeling by introducing ideas from fuzzy sets and related soft computing technologies. One is with the introduction of fuzzy graphs representing the networks. A second is the use of the fuzzy set based paradigm of computing with words to provide a bridge between...
We have introduced the linguistic weighted power mean and its computation via a generalization of the Karnick-Mendel algorithm. This new family of aggregation operators admits interval type-2 fuzzy membership functions for both its inputs and its weights to account for imprecise knowledge of these quantities. The simplest member of this family, the linguistic weighted average, is the primary instantiation...
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