Parallel Clustering Algorithm for the k-medoids Problem in High-dimensional Space for Large-scale Datasets-Reference-Cited by-同舟云学术

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Parallel Clustering Algorithm for the k-medoids Problem in High-dimensional Space for Large-scale Datasets

Published:2023-08-14 Issue: Volume: Page:
ISSN:
Container-title:2023 19th International Asian School-Seminar on Optimization Problems of Complex Systems (OPCS)
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Author:

Vandanov Sergey¹,Plyasunov Aleksandr²,Ushakov Anton³

Affiliation:

1. Novosibirsk State University,Department of Physics,Novosibirsk,Russia

2. Sobolev Institute of Mathematics of SB RAS,Lab. of Mathematical Models of Decision Making,Novosibirsk,Russia

3. Matrosov Institute for System Dynamics and Control Theory of SB RAS,Irkutsk,Russia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10275322/10275323/10275752.pdf?arnumber=10275752

Reference16 articles.

1. K-nearest neighbor search: Fast GPU-based implementations and application to high-dimensional feature matching

2. Parallel euclidean distance matrix computation on big datasets;angeletti;2019 paper hal-02047514f,0

3. Solving large p-median clustering problems by primal–dual variable neighborhood search

4. Billion-Scale Similarity Search with GPUs

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