Comparative of prim’s and boruvka’s algorithm to solve minimum spanning tree problems

Marpaung, Faridawaty; Arnita., .

doi:10.1088/1742-6596/1462/1/012043

Cited by 8 publications

(4 citation statements)

References 3 publications

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“…For Adaptive HDBSCAN, the minimum spanning tree is built adaptively based on the number of points being clustered. For smaller numbers of points, Prim's algorithm [19] is used, whereas Boruvka's algorithm [20] is used for a larger number of data points.…”

Section: Adaptive Hdbscan Implementationmentioning

confidence: 99%

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Adaptive Hierarchical Density-Based Spatial Clustering Algorithm for Streaming Applications

Vijayan

Aziz

2022

Telecom

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Clustering algorithms are commonly used in the mining of static data. Some examples include data mining for relationships between variables and data segmentation into components. The use of a clustering algorithm for real-time data is much less common. This is due to a variety of factors, including the algorithm’s high computation cost. In other words, the algorithm may be impractical for real-time or near-real-time implementation. Furthermore, clustering algorithms necessitate the tuning of hyperparameters in order to fit the dataset. In this paper, we approach clustering moving points using our proposed Adaptive Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm, which is an implementation of an adaptive approach to building the minimum spanning tree. We switch between the Boruvka and the Prim algorithms as a means to build the minimum spanning tree, which is one of the most expensive components of the HDBSCAN. The Adaptive HDBSCAN yields an improvement in execution time by 5.31% without depreciating the accuracy of the algorithm. The motivation for this research stems from the desire to cluster moving points on video. Cameras are used to monitor crowds and improve public safety. We can identify potential risks due to overcrowding and movements of groups of people by understanding the movements and flow of crowds. Surveillance equipment combined with deep learning algorithms can assist in addressing this issue by detecting people or objects, and the Adaptive HDBSCAN is used to cluster these items in real time to generate information about the clusters.

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Section: Adaptive Hdbscan Implementationmentioning

confidence: 99%

“…Prim's algorithm starts with a single node and works its way through all of the adjacent nodes, exploring all of the connected edges along the way. There are no cycles on the edges with the smallest weights [19]. The pseudocode for Prim's algorithm is described in Figure 3:…”

Section: Prim's Algorithmmentioning

confidence: 99%

Adaptive Hierarchical Density-Based Spatial Clustering Algorithm for Streaming Applications

Vijayan

Aziz

2022

Telecom

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“…Traveling Salesman [92] Traveling Salesman [93] Multiple Traveling Salesman [94] Bottleneck Traveling Salesman [95] Cutting Stock [96] Cutting Stock [97] 2D Cutting [98] Packing [99] Packing [100] 2D Packing [101] Bin Packing [102] Knapsack [103] Knapsack [104] Subset Sum [105] Unbounded Knapsack [105] Bounded Knapsack [106] Multiple Knapsack [107] Quadratic Knapsack [108] Scheduling [109] Scheduling [110] Production Scheduling [111] Workforce Scheduling [112] Job-Shop Scheduling [113] Precedence Constrained Scheduling [114] Educational Timetabling [115] Educational Timetabling [116] Facility Location [117] Assignment [118] Quadratic Assignment [119] Spanning Tree [120] Maximum Leaf Spanning Tree [121] Degree Constrained Spanning Tree [122] Minimum Spanning Tree [123] Boolean Satisfiability [124] Boolean Satisfiability [125] Covering [126] Minimum Vertex Cover [127] Set Cover [128] Exact Cover [129] Minimum Edge Cover [130] Vehicle Routing [131] Vehicle Routing…”

Section: Type Problemmentioning

confidence: 99%

A Systematic Review of Hyper-Heuristics on Combinatorial Optimization Problems

et al. 2020

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Hyper-heuristics aim at interchanging different solvers while solving a problem. The idea is to determine the best approach for solving a problem at its current state. This way, every time we make a move it gets us closer to a solution. The problem changes; so does its state. As a consequence, for the next move, a different solver may be invoked. Hyper-heuristics have been around for almost 20 years. However, combinatorial optimization problems date from way back. Thus, it is paramount to determine whether the efforts revolving around hyper-heuristic research have been targeted at the problems of the highest interest for the combinatorial optimization community. In this work, we tackle such an endeavor. We begin by determining the most relevant combinatorial optimization problems, and then we analyze them in the context of hyper-heuristics. The idea is to verify whether they remain as relevant when considering exclusively works related to hyper-heuristics. We find that some of the most relevant problem domains have also been popular for hyper-heuristics research. Alas, others have not and few efforts have been directed towards solving them. We identify the following problem domains, which may help in furthering the impact of hyper-heuristics: Shortest Path, Set Cover, Longest Path, and Minimum Spanning Tree. We believe that focusing research on ways for solving them may lead to an increase in the relevance and impact that hyperheuristics have on combinatorial optimization problems.

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“…[25], MYA probleminde Kruskal algoritmasını kullanarak bir yer altı kablo tesisatı için kablo kanallarının üç besleme ucundan ihtiyaç duyulan bir inşaat sahasındaki dört konuma gönderilmesindeki ulaştırma sorununa odaklanmıştır. Marpaung, F. [26], MYA problemlerini çözmek için Prim ve Boruvka algoritmalarını karşılaştırmış ve Prim algoritmasının daha verimli olduğunu göstermiştir. Rachmawati ve Pakpahan [27], MYA probleminin çözümünde Kruskal ve Boruvka algoritmalarını kullanarak karşılaştırmalı bir analiz yapmıştır.…”

Section: Introductionunclassified

Minimum Yayılan Ağaç (MYA) Problemi: Denizli İli Hafif Raylı Sistem Proje Önerisi için Minimum Mesafeli Hat Belirleme

AKAY

Tuş

2022

Demiryolu Mühendisliği

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Öz: Şehir içi ulaşımda hat (güzergâh) belirleme süreci, önemli bir konudur ve bu süreç doğru bir şekilde planlanırsa yapılan yatırım veya kurulan yeni sistem, trafikte iyileşme sağlayacaktır. Bu çalışmanın amacı, şehir içi ulaşımda rahatlık, kolaylık ve zaman tasarrufu sağlamak için Denizli Belediyesi tarafından karayolu ulaşım sistemine alternatif olarak önerilen hafif raylı sistem projesi kapsamında minimum mesafeli bir hat belirlemektir. Bu nedenle hat belirleme, graf yani ağ yapısındaki sistemlerin tasarlanmasında kullanılabilen bir Minimum Yayılan Ağaç (MYA) problemi olarak ele alınmıştır. Problemin çözümünde Prim, Kruskal algoritmaları ve matris yöntemi kullanılmıştır. Her üç yöntem ile optimal sonuç elde edilmiştir. Minimum MYA mesafesi 29,09 km'dir. Ancak söz konusu yöntemler için süreçler; işlem kolaylığı, iterasyon sayısı, karmaşıklıkların minimuma indirilmesi açısından değerlendirildiğinde, daha avantajlı olan matris yönteminin gerçek hayat MYA problemlerinde kullanılmasının daha uygun olacağı sonucuna varılmıştır.

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Comparative of prim’s and boruvka’s algorithm to solve minimum spanning tree problems

Cited by 8 publications

References 3 publications

Adaptive Hierarchical Density-Based Spatial Clustering Algorithm for Streaming Applications

Adaptive Hierarchical Density-Based Spatial Clustering Algorithm for Streaming Applications

A Systematic Review of Hyper-Heuristics on Combinatorial Optimization Problems

Minimum Yayılan Ağaç (MYA) Problemi: Denizli İli Hafif Raylı Sistem Proje Önerisi için Minimum Mesafeli Hat Belirleme

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