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This paper proposes an approach for clustering belief functions. The approach is composed of agglomerative clustering and how to determine the cluster number. The former one is achieved by taking belief distance as dissimilarity measure between two belief functions and selecting complete-link algorithm to measure the dissimilarity between two clusters. The latter one is completed by utilizing metaconflict...
Clustering is a method of unsupervised learning, and a common technique for statistical data analysis used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics a novel algorithm based on clustering to extract rules from neural networks is proposed. After neural networks have been trained and pruned successfully, inner-rules are generated by...
In this paper, orthogonal frequency-division multiple-access (OFDMA) systems with dynamic deterministic (as opposed to pseudorandom) allocation of subcarriers to users to exploit multiuser diversity are investigated. Previously published work on dynamic multiuser subcarrrier allocation for OFDMA systems with single-input-single-output (SISO) channels are surveyed. A near-optimal low-complexity algorithm...
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