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Software-Defined Networking (SDN) is a new paradigm that is attracting significant attention from both academy and industry. In recent years, increasing research efforts have addressed the conception of programming interfaces—northbound APIs—which allow business-applications to properly communicate with SDN controllers and program the network. However, most of current SDN controllers still offer low-level...
Recently, kernel-based clustering in feature space has shown to perform better than conventional clustering methods in unsupervised classification. In this paper, a partitioning clustering method in kernel-induce feature space for symbolic interval-valued data is introduced. The distance between an item and its prototype in feature space is expanded using a two-component mixture kernel to handle intervals...
Kernel k-means algorithms have recently been shown to perform better than conventional k-means algorithms in unsupervised classification. In this paper we present is an extension of kernel k-means clustering algorithm for symbolic interval data. To evaluate this method, experiments with synthetic and real interval data sets were performed and we have been compared our method with a dynamic clustering...
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