A New Algorithmic Approach to Finding Minimum Spanning Tree

Khan, A. Nayeemulla; Aesha, Afrida Anzum; Sarker, Juthi

doi:10.1109/ceeict.2018.8628095

Cited by 8 publications

(3 citation statements)

References 14 publications

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“…Though presented visually as seen in Figure 2, the implementation of the MST algorithm is based on Kruskal's Algorithm (Biswas et al, 2016;Khan et al, 2018). This performs the identification of the most optimal path to follow, observing the algorithm flow presented in Algorithm 1. if FIND-SET(u) ≠ FIND-SET(v):…”

Section: Figure 3 Course Graph Weighingmentioning

confidence: 99%

Optimizing the University Student Advising Process through Weighted Minimum Spanning Tree Algorithm

Sabado

2023

IJCSR

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Purpose -This paper discusses a new approach combining the network graph theory on minimum spanning tree and ensemble-based machine learning algorithm for weight generation to solve an optimization problem for common university advising processes. University advising requires an expert's validation to identify an optimal set of courses that can be recommended to students to finish their program of study at an earliest time while giving heavy considerations on the volume of workload for a given semester.Method -The study was analyzed utilizing the historical dataset from students' academic profiles from a local university from the Philippines. The process undergoes a set of weight generation, MST model application and ensemble scoring method.Results -The proposed algorithm was validated using the expert validated advising results which yielded to 92% accuracy.Conclusion -This paper attempts to develop a solution to the tedious process of student advising through representing the curriculum in a directed graph and implementing a minimum spanning tree algorithm with ML-based weight edges. 1724Recommendations -To address the potential reasons on the detected accuracies, it is further recommended to include all other constraints such as accommodating corequisites between courses.Practical Implications -The proposed algorithm can be implemented in a web-based application or integrated in an enrollment information system for a more justifiable appreciation of the envisioned process improvement.

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Section: Figure 3 Course Graph Weighingmentioning

confidence: 99%

Optimizing the University Student Advising Process through Weighted Minimum Spanning Tree Algorithm

Sabado

2023

IJCSR

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“…Another proposed method involves creating a reduced graph by employing vertex merging technology [19]. Meanwhile, in [7], the number of minimum spanning tree (MST) edges is reduced through the disjoint algorithm. This study also examines the new modern network fabric technology.…”

Section: Related Workmentioning

confidence: 99%

Layer 2 Path Evaluation System using Machine Learning

Sebakor

2021

ECTI-EEC

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Is it strange that the spanning tree protocol (STP) has been the only thing used to defend the Layer-2 backbone against looping? Do we trust it? For several decades, the campus backbone has often been an unsuspected problem, one of which is STP failure. Meanwhile, the MAC address flapping is probably a feasible issue for modern network fabrics. According to the serious Layer-2 issues, particularly the legacy switches extended STP design, this work uses the notion of a software-defined network fashion to evaluate the traditional and modern networks. Through the MAC address lookup of all bridge devices, this work proposes the Layer-2 evaluation system (LES), which uses a novel approach known as support supervised learning to create the data preparation for machine learning. Additionally, the LES enabled network administrators to determine their backbones. This study is intended to evaluate the potential slowdown network caused by MAC address problems. Furthermore, this work investigates the proposed method in a real network, and it also covers the evaluation and performance of our proposed method.

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“…The disjoint set data structures were used to determine the minimum spanning tree of a graph by Khan et al (3) . A weight was assigned to each node for finding the candidate edge which was initially set to ∞.…”

Section: Introductionmentioning

confidence: 99%

AMST: Accelerated Minimum Spanning Tree for Scalable Data

Mohapatra,

Adhikari,

Ray

et al. 2023

IJST

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Objectives: A traditional Minimum Spanning Tree (MST) is quite complex for large datasets concerning time and space complexities. So an Accelerated MST (AMST) is proposed for scalable data. Methods: AMST uses the k-means clustering to divide large data into a bunch of small-size data. The Delaunay Triangulation is applied to find the relationship among the data in O (nlogn) time for n points as compared to earlier approaches. A mid-point heuristic is designed to combine all clustered MSTs to get the MST for the original data. A refinement process is used for any mislaid of minimum weight edges. Findings: Several desirable properties of the AMST have been developed and compared with the results of existing literature. AMST is quite competitive in terms of time complexities, least weight error rate and accuracy for both large and small-size datasets. The mathematical analysis of the time complexity O (nlogn) proves that the proposed AMST has a 95% faster execution time than others. The proposed AMST has the least Weight Error Rate than other approaches which indicates that the cost is close to the exact Algorithms. The accuracy of the AMST is an average of 93% in different clustering accuracy indices which is similar to other approaches. Novelty: The execution time and accuracy prove that a faster MST can be developed to construct pins wire length routing in the promising field of VLSI (Very Large-Scale Integration) physical design.

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A New Algorithmic Approach to Finding Minimum Spanning Tree

Cited by 8 publications

References 14 publications

Optimizing the University Student Advising Process through Weighted Minimum Spanning Tree Algorithm

Optimizing the University Student Advising Process through Weighted Minimum Spanning Tree Algorithm

Layer 2 Path Evaluation System using Machine Learning

AMST: Accelerated Minimum Spanning Tree for Scalable Data

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