2020
DOI: 10.1007/s40747-020-00191-y
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Enhanced synchronization-inspired clustering for high-dimensional data

Abstract: The synchronization-inspired clustering algorithm (Sync) is a novel and outstanding clustering algorithm, which can accurately cluster datasets with any shape, density and distribution. However, the high-dimensional dataset with high dimensionality, high noise, and high redundancy brings some new challenges for the synchronization-inspired clustering algorithm, resulting in a significant increase in clustering time and a decrease in clustering accuracy. To address these challenges, an enhanced synchronization-… Show more

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Cited by 15 publications
(5 citation statements)
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References 27 publications
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“…In recent years, some classic clustering algorithms have been developed and expanded, such as modified k-means clustering algorithm [13], density spatial clustering algorithm [14], K-nearest neighbor decision clustering algorithm [15], density deviation multi-peaks automatic clustering algorithm [16], and synchronization-inspired clustering algorithm [17]. The classic clustering algorithms are effective when the data structures are the combination of simple form and the features are representative.…”
Section: Classic Clusteringmentioning
confidence: 99%
“…In recent years, some classic clustering algorithms have been developed and expanded, such as modified k-means clustering algorithm [13], density spatial clustering algorithm [14], K-nearest neighbor decision clustering algorithm [15], density deviation multi-peaks automatic clustering algorithm [16], and synchronization-inspired clustering algorithm [17]. The classic clustering algorithms are effective when the data structures are the combination of simple form and the features are representative.…”
Section: Classic Clusteringmentioning
confidence: 99%
“…Manuscript received: 2023-02-21; revised: 2023-05-12; accepted: 2023-05-15 is responsible for converting a strongly coupled HN from a high-dimensional space to a discrete node vector in a low-dimensional space, and these vectors can preserve the rich semantic and structural information of this HN to the maximum possible extent [2,3] . HN representation learning can be used to mine valuable information hidden in an HN, and it has been proved to be of considerable assistance to many downstream network analysis tasks, such as node classification, link prediction, and visualization [4][5][6] .…”
Section: Introductionmentioning
confidence: 99%
“…It represents the mainstream methods used for reactive voltage partitioning. However, certain hierarchical clustering-based methods [13][14][15] primarily focus on network topology, lacking a comprehensive integration of the electrical characteristics and actual operating conditions of the power grid. Additionally, partitioning clustering-based methods [16,17] may encounter difficulties when dealing with datasets containing non-convex cluster shapes.…”
Section: Introductionmentioning
confidence: 99%