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We develop a new non-parametric hierarchical information theoretic clustering algorithm based on implicit estimation of cluster densities using k-nearest neighbors (k-nn). Compared to a kernel-based procedure, our k-nn approach is very robust with respect to the parameter choices, with a key ability to detect clusters of vastly different scales. Of particular importance is the use of two different...
Developing efficient methods for monitoring and identifying species of birds and anurans in natural environments are an imperative, in order to attend the concern caused by amphibian decline and trends in decreasing bird population sizes. In this work, a prospective solution to contribute to the mentioned problem is presented by an infrastructure implementation designed to deploy applications in disaster...
A new Differential Evolution (DE) that incorporates fuzzy control and k-nearest neighbors algorithm to determine the terminating condition is proposed. A technique called Iteration Windows is introduced to govern the number of iteration in each searching stage. The size of the iteration windows is controlled by a fuzzy controller, which uses the information provided by the k-nearest neighbors system...
As a fingerprint match method, k-nearest neighbors (KNN) has been widely applied for indoor location in Wireless Local Area Networks (WLAN), but its performance is sensitive to number of neighbors k and positions of reference points (RPs). So fuzzy c-means (FCM) clustering algorithm is applied to improve KNN, which is the KNN-FCM hybrid algorithm presented in this paper. In the proposed algorithm,...
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