A depth-based modification of the k-nearest neighbour method

Vencálek, Ondřej; Hlubinka, Daniel

doi:10.14736/kyb-2021-1-0015

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…, k The kmeans and k-nearest neighbor algorithms are well-known clustering algorithms. Various improvements of these algorithms are still being made [31]. In this paper, we use the k-means clustering algorithm.…”

Section: Constrained K-means Algorithmmentioning

confidence: 99%

Constrained k-means algorithm for resource allocation in mobile cloudlets

Alguliyev¹,

Alıguliyev²,

Alakbarov³

2023

Kybernetika

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With the rapid increase in the number of mobile devices connected to the Internet in recent years, the network load is increasing. As a result, there are significant delays in the delivery of cloud resources to mobile users. Edge computing technologies (edge, cloudlet, fog computing, etc.) have been widely used in recent years to eliminate network delays. This problem can be solved by allocating cloud resources to the cloudlets that are close to users. The article proposes a clustering-based model for the optimal allocation of cloud resources among cloudlets. The proposed model takes into account user activity, usage frequency of cloud resources, the physical distance between users and cloud resources, as well as the storage capacity of cloudlets for optimal allocation of cloud resources in cloudlets. The proposed model was formalized as a constrained k-means method and an algorithm was developed to solve it. The MATLAB 2022a toolkit was used to evaluate the efficiency of the proposed algorithm. The obtained results revealed that the algorithm is promising.

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Section: Constrained K-means Algorithmmentioning

confidence: 99%

Constrained k-means algorithm for resource allocation in mobile cloudlets

Alguliyev¹,

Alıguliyev²,

Alakbarov³

2023

Kybernetika

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“…Graph embedding is a map between high-dimensional, highly structured data and low-dimensional vector space that preserves the structural information of all nodes in the network. The embedding process gains importance due to the growing number of applications that benefit from network data in a broad range of machine learning domains, such as natural language processing (NLP), bioinformatics [43], social network analysis [26,38], and recently as building blocks of reinforcement learning algorithms [4,42].…”

Section: Introductionmentioning

confidence: 99%

TPM: Transition probability matrix -- Graph structural feature based embedding

Mohammed¹,

Gündüç²

2023

Kybernetika

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this work, Transition Probability Matrix (TPM) is proposed as a new method for extracting the features of nodes in the graph. The proposed method uses random walks to capture the connectivity structure of a node's close neighborhood. The information obtained from random walks is converted to anonymous walks to extract the topological features of nodes. In the embedding process of nodes, anonymous walks are used since they capture the topological similarities of connectivities better than random walks. Therefore the obtained embedding vectors have richer information about the underlying connectivity structure. The method is applied to node classification and link prediction tasks. The performance of the proposed algorithm is superior to the state-of-the-art algorithms in the recent literature. Moreover, the extracted information about the connectivity structure of similar networks is used to link prediction and node classification tasks for a completely new graph.

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