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In the proposed Popularity Peak Clustering (PPC) algorithm, we assign to each data sample x_i, its popularity \pi _i and its distance \delta _i to its direct superior. In most DPC-based algorithms, the direct superior of a sample is assumed to be its nearest higher-density neighbor.
May 21, 2024
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A popularity peak clustering algorithm that is based on a more robust notion of density called popularity that can recognize clusters regardless of their ...
TL;DR: This work proposes a fast density peaks algorithm that solves the time complexity problem, and maintains the generality of density peaks, which allows ...
An efficient clustering algorithm based on searching popularity peaks - OUCI
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An efficient clustering algorithm based on searching popularity peaks · List of references · Publications that cite this publication.
The popularity of a sample is computed according to the number, similarity and popularity of points that have the sample in their k-nearest neighbors. The ...
The popularity concept has some properties that help in handling challenges like identifying cluster centers in sparse regions and handling situations with ...
Source code for the "popularity peaks clustering algorithm" [An efficient clustering algorithm based on searching popularity peaks].
Jun 1, 2022 · Density Peak Clustering (DPC) is a useful algorithm for grouping data points based on their closeness and density. It is widely used in various ...
A data with two clusters of widely differing densities (Fig. 2) (a · An efficient clustering algorithm based on searching popularity peaks. Article. Full-text ...
As a clustering approach based on density, Density Peaks Clustering algorithm (DPC) has conspicuous superiorities in searching and finding density peaks.