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The clusters tend to have vague or imprecise boundaries in some fields such as web mining, since clustering has been widely used. Decision-theoretic rough set model (DTRSM) is a typical probabilistic rough set model, which has the ability to deal with imprecise, uncertain, and vague information. Therefore, a novel clustering algorithm based on the DTRSM is proposed in this paper, which can decide...
In this paper, we incorporate clustering techniques into distributed consensus algorithms for faster convergence and better energy efficiency. Together with a simple distributed clustering algorithm, we design cluster-based distributed consensus algorithms in forms of both fixed linear iteration and randomized gossip. The time complexity of the proposed algorithms is presented in terms of metrics...
Recognition of multiple moving objects is a very important task for achieving user-cared knowledge to send to the base station in wireless video-based sensor networks. However, video based sensor nodes, which have constrained resources and produce huge amount of video streams continuously, bring a challenge to segment multiple moving objects from the video stream online. Traditional efficient clustering...
Outlier detection is important in many fields. The concept about outlier factor of object is extended to the case of cluster. Based on outlier factor of cluster, a clustering-based outlier detection method, named CBOD, is presented. The method consists of two stages, the first stage cluster dataset by one-pass clustering algorithm and second stage determine outlier cluster by outlier factor. The time...
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