A distance coefficient-based algorithm for k-center selection in wireless sensor networks

Sung, Tien-Wen; Kong, Lingping; Tsai, Pei‐Wei; Pan, Jeng‐Shyang

doi:10.1109/icce-china.2017.7991111

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…The K-means clustering algorithm cannot extract data features effectively when processing highdimensional data directly, and problems also occur when it randomly selects initial clustering centers and specifies the number of clustering in advance. These problems have been researched in numerous papers over the recent decades, as discussed elsewhere [25][26][27]. Therefore, we propose an improved method using the DPC algorithm.…”

Section: Definition 2 Local Density ρ I Based On Cosine Similarity (Gaussian Kernel)mentioning

confidence: 99%

High-Dimensional Text Clustering by Dimensionality Reduction and Improved Density Peak

Sun

Platoš

2020

Wireless Communications and Mobile Computing

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This study focuses on high-dimensional text data clustering, given the inability of K-means to process high-dimensional data and the need to specify the number of clusters and randomly select the initial centers. We propose a Stacked-Random Projection dimensionality reduction framework and an enhanced K-means algorithm DPC-K-means based on the improved density peaks algorithm. The improved density peaks algorithm determines the number of clusters and the initial clustering centers of K-means. Our proposed algorithm is validated using seven text datasets. Experimental results show that this algorithm is suitable for clustering of text data by correcting the defects of K-means.

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Section: Definition 2 Local Density ρ I Based On Cosine Similarity (Gaussian Kernel)mentioning

confidence: 99%

High-Dimensional Text Clustering by Dimensionality Reduction and Improved Density Peak

Sun

Platoš

2020

Wireless Communications and Mobile Computing

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“…When a non-intermediate variable appears at multiple locations, only the earliest one is retained. The k-center point algorithm is used to divide the non-intermediate variables of short variable distance into the same microprocessor core [16]. Since the "hand-in" data interaction takes precedence over the "data-pipeline" data interaction, the non-intermediate variables is allocated following the principle of minimizing the distance of non-intermediate variables in adjacent PEs.…”

Section: Data Schedulingmentioning

confidence: 99%

A Novel FPGA-Based Real-Time Simulator for Micro-Grids

Bingda

Jin

et al. 2017

Energies

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Abstract:To meet the requirements of micro-grid real-time simulation, a novel real-time simulator for micro-grids based on Field-Programmable Gate Array (FPGA) and orders (FO-RTDS) is designed. We describe the design idea of the real-time solver and the order generator. Multi-valued parameter prestorage and multi-rate simulation are introduced to reduce the computational pressure. The data scheduling is carried out following the principle of saving the resources and the minimizing the average distance between variables. An example is performed on XC7VX690T-2FFG1761 chip, which proves the novel FO-RTDS method greatly improves the scale of real-time simulation of micro-grids.

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SNP selection based on k-center algorithm

Cao

2019

2019 6th International Conference on Systems and Informatics (ICSAI)

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A distance coefficient-based algorithm for k-center selection in wireless sensor networks

Cited by 3 publications

References 5 publications

High-Dimensional Text Clustering by Dimensionality Reduction and Improved Density Peak

High-Dimensional Text Clustering by Dimensionality Reduction and Improved Density Peak

A Novel FPGA-Based Real-Time Simulator for Micro-Grids

SNP selection based on k-center algorithm

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