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Although fuzzy c-means algorithm has shown great capability to spherical clusters, it can not perform very well on non-spherical data sets yet. To deal with this problem, kernel-based fuzzy clustering has been presented by mapping data points into a high-dimensional Hilbert space with kernel functions. However, the computational complexity of kernel matrix is always quadratic, usually makes kernel...
Bridging nodes serve as junction or link between two or more clusters of a network or graph. In this paper, we propose a method for detecting the bridging nodes by analyzing membership coefficients (obtained from Fuzzy Communities or Clusters) and a flexible cluster size dependent threshold value. Spectral methods, which are based on eigen-decomposition are employed for obtaining fuzzy clusters.
Cluster is bunch of similar items. Unsupervised classification of patterns into clusters is known as clustering. It is useful in knowledge discovery in data. Clustering is able to deal with different data types. Fuzzy rules are used for data intelligence illustration purpose. User gets highly interpretable discovered clusters using fuzzy rules. To generate accurate fuzzy rules triangular membership...
This paper expatiates on the improved fuzzy c-means (FCM) clustering algorithm. In the algorithm, the membership values are determined via the improved method, and the number of the centers of FCM clustering are determined via the number of the peaks of the two-dimensional histogram on the gray-level values of pixels and gradient values of pixel neighborhoods. The application to segmentation of a...
Existing clustering ensemble algorithms for partitioning categorical data only apply to know the generating process of clustering members very well. In order to broaden the application of clustering ensemble, a fuzzy clustering ensemble algorithm for partitioning categorical data is proposed in this paper. The proposed algorithm makes use of relationship degree between different attributes for pruning...
The algorithm of locally adaptive clustering for high dimensional data (LAC) processes soft subspace clustering by local weightings of features. To solve the localization of LAC in specifying the number of clusters, this paper reworks the validity index for fuzzy clustering to evaluate the clustering results of LAC. Compared with real clustered data, the method is proved feasible. In the new algorithm,...
As modeling and visualization applications proliferate, there arises a need to reduce three dimensional unorganized data points in reverse engineering. To meet the demand for both geometric and engineering fidelity of the reduction, a fuzzy-clustering-based reduction method is presented. As an effective extension to the existing pure geometric reduction methods, a hybrid heuristic is introduced. It...
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