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The density peak based clustering algorithm is a recently proposed clustering approach. It uses the local density of each data and the distance to the nearest neighbor with higher density to isolate and identify the cluster centers. After the cluster centers are identified, the other data are assigned labels equaling to those of their nearest neighbors with higher density. This algorithm is simple...
The density peak based clustering algorithm is a simple yet effective clustering approach. This algorithm firstly calculates the local density of each data and the distance to the nearest neighbor with higher density. Based on the assumption that cluster centers are density peaks and they are relatively far from each other, this algorithm isolates the candidates of cluster centers from the non-center...
The recently proposed clustering algorithm based on density peaks is reported to generate very good clustering results. This algorithm is simple and efficient, and can be used to generate clusters of arbitrary shapes. However, the performance of this algorithm relies on the selection of the kernel in local density calculation. The original density peak based algorithm uses the cutoff kernel and Gaussian...
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