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In order to improve the accuracy of fuzzy clustering of datasets with missing attributes, a hybrid clustering algorithm is proposed in this paper. The algorithm determines the estimation intervals of missing attributes by the Nearest Neighbor method and the ranges of cluster centers by seeking the maximum and minimum of attributes, which constitute the constraint space together. Under the space, the...
The evaluation of clustering quality has proven to be a difficult task. While it is generally agreed that application specific human assessment can provide a reasonable gold standard for clustering evaluation, the use of human assessors is not practical in many real situations. As a result, machine computable internal clustering quality measures (CQMs) are often used in the evaluation process. However,...
This paper presents a new version of Davies-Bouldin index for clustering validation through the use of a new distance based on density. This new distance, called cylindrical distance, is used as a similarity measurement between the means of the clusters, in order to overcome the limitations of the Euclidean distance. The cylindrical distance takes into account the distribution of the data set, using...
Clustering can be especially effective where the data is irregular, noisy and/or not differentiable. A major obstacle for many clustering techniques is that they are computationally expensive, hence limited to smaller data volume and dimension. We propose a lightweight swarm clustering solution called Rapid Centroid Estimation (RCE). Based on our experiments, RCE has significantly quickened optimization...
Routing policies and the complexity of network give rise to violations of the Triangle Inequality with respect to delay (Round-Trip Time) in the Internet. Most of the network coordinate (NC) systems, for example Vivaldi, suffer from inaccurate distance estimation due to such Triangle Inequality Violations (TIVs). In this abstract, we propose a methodology to overcome TIVs by introducing medium in...
Image segmentation algorithm based on fuzzy c-means clustering is an important algorithm in the image segmentation field. It has been used widely. However, it is not successfully to segment the noise image because the algorithm disregards of special constraint information. It only considers the gray information. Therefore, we proposed a weighed FCM algorithm based on Gaussian kernel function for image...
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