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This paper proposes a generalized kernel fuzzy clustering model and investigates the features of the proposed model. An additive clustering model has been proposed that considers the overlapping of clusters whose target data is similarity data. In addition, by introducing the concept of a fuzzy cluster to the additive clustering model, an additive fuzzy clustering model has been proposed. In these...
This paper proposes a fuzzy clustering method under the intrinsically classified structure of data through dissimilarity of objects at each variable. In order to extract the classification structure, the variable-based fuzzy clustering method is exploited and the degree of classification for each object with respect to each variable is defined. This degree shows individually classified power of an...
This paper proposes a clustering method for asymmetric similarity data. In this method, systematic asymmetry in the data is explained by using self-similarity of objects. We exploit an additive fuzzy clustering model for capturing the classification structure in the data. Moreover, the symmetric similarity data is restored by using the result of the clustering method. Therefore, we can exploit many...
A fuzzy clustering method considering weights of variable-based dissimilarities over objects in the subspace of the objectpsilas space is proposed. In order to estimate the weights, we propose two methods. One is a method in which a conventional fuzzy clustering method is directly used for the variable-based dissimilarity data. The other is to use a new objective function. Exploiting the weights,...
This paper presents a general class of fuzzy cluster loading models. Fuzzy clustering was devised to obtain a natural clustering result vritli a certain degree of belongingness of objects to clusters. Although the concept is rather intuitively defined, it is well known that fuzzy clustering has the power to reveal the complex structure of real data. Instead of the representativeness of fuzzy clustering,...
This paper presents two methods based on self-organized dissimilarity. The first is an implemented fuzzy clustering and the second is a hybrid method of fuzzy clustering and multidimensional scaling (MDS). Specifically, a self-organized dissimilarity is defined that uses the result of fuzzy clustering in such a way that the dissimilarity of objects is influenced by the dissimilarity of the classification...
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