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Previous clustering ensemble algorithms usually use a consensus function to obtain a final partition from the outputs of the initial clustering. In this paper, we propose a new clustering ensemble method, which generates a new feature space from initial clustering outputs. Multiple runs of an initial clustering algorithm like k-means generate a new feature(More)
Clustering ensembles have emerged as a powerful method for improving both the robustness and the stability of unsupervised classification solutions. However, finding a consensus clustering from multiple partitions is a difficult problem that can be approached from graph-based, combinatorial or statistical perspectives. We offer a probabilistic model of(More)
Hybrid CDN-P2P networks blend Content Delivery Networks (CDN) and Peer-to-Peer (P2P) networks to overcome their shortcomings. Replica placement in these networks is still an open problem. Hierarchical structure of these networks makes it inefficient to use available replica placement strategies specialized to CDN or P2P network domains. In this work, we(More)
In this paper, a new combinational method for improving the recognition rate of multiclass classifiers is proposed. The main idea behind this method is using pairwise classifiers to enhance the ensemble. Because of more accuracy of them, they can decrease the error rate in error-prone feature space. Firstly, a multiclass classifier has been trained. Then,(More)
High performance clusters, which are established by connecting many computing nodes together, are known as one of main architectures to obtain extremely high performance. Currently, these systems are moving from multi-core architectures to many-core architectures to enhance their computational capabilities. This trend would eventually cause network(More)
This paper presents a new algorithm for background subtraction that can model the background image from a sequence of images, even if there are foreground objects in each image frame. In contrast with Gaussian Mixture Model algorithm, in our proposed method the problem of distinguishing between background and foreground kernels becomes trivial. The key idea(More)