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With the explosive growth number of services in cloud computing environment, how to accurately and rapidly discover the services that can meet user's functional and nonfunctional requirements is a challenging subject. Aiming at issues of service inefficiencies and low precision in the existing service discovery methods, a model for service discovery based on functions and QoS clustering is proposed...
Node in WSN has its own local clock, and it's difficult to achieve long-term time synchronization between nodes due to some internal and external factors. Within the factors, some internal attackers (group members) report false clock references to their neighbor nodes. Considering the special circumstance that some nodes of the wireless sensor network are faulty, an improved HRTS algorithm named T-HRTS...
Node in WSN has its own local clock, and it's difficult to achieve long-term time synchronization between nodes due to some internal and external factors. Within the factors, a special one is Byzantine general problem which will be discussed in this article. Considering the special circumstance that some nodes of the wireless sensor network have the same clock, an improved HRTS algorithm which based...
This work presents an Enhanced Objective Cluster Analysis-based fuzzy iterative learning approach for T-S fuzzy modeling. In this method, the Enhanced Objective Cluster Analysis including the Dipole Partition, the Relative Dissimilarity Measure and the Enhanced Consistency Criterion are incorporated with the Fuzzy - Means algorithm for the robust and compact fuzzy partition in the input space. For...
In this study, a novel iterative optimization clustering algorithm is proposed by using a manifold distance based dissimilarity metric which can measure the geodesic distance along the manifold and a criterion function which can express the clustering target, that is the samples in the same cluster being somehow more similar than samples in different one. The steps of the algorithm are discussed in...
Clustering with constraints is an active area in machine learning and data mining. In this paper, a semi-supervised kernel-based fuzzy C-means algorithm called PCKFCM is proposed which incorporates both semi-supervised learning technique and the kernel method into traditional fuzzy clustering algorithm. The clustering is achieved by minimizing a carefully designed objective function. A kernel-based...
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