scholarly journals Segmentation of Fuzzy and Touching Cells Based on Modified Minimum Spanning Tree and Concave Point Detection

10.5772/65029 ◽  
2016 ◽  
Author(s):  
Weixing Wang ◽  
Liqun Lin
Keyword(s):  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Point Detection

scholarly journals Consistent and powerful non-Euclidean graph-based change-point test with applications to segmenting random interfered video data

2018 ◽  
Vol 115 (23) ◽  
pp. 5914-5919 ◽  
Author(s):  
Xiaoping Shi ◽  
Yuehua Wu ◽  
Calyampudi Radhakrishna Rao
Keyword(s):  
Change Point ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Hamiltonian Path ◽  
Video Data ◽  
Change Point Detection ◽  
Change Point Test ◽  
Point Test ◽  
Point Detection

The change-point detection has been carried out in terms of the Euclidean minimum spanning tree (MST) and shortest Hamiltonian path (SHP), with successful applications in the determination of authorship of a classic novel, the detection of change in a network over time, the detection of cell divisions, etc. However, these Euclidean graph-based tests may fail if a dataset contains random interferences. To solve this problem, we present a powerful non-Euclidean SHP-based test, which is consistent and distribution-free. The simulation shows that the test is more powerful than both Euclidean MST- and SHP-based tests and the non-Euclidean MST-based test. Its applicability in detecting both landing and departure times in video data of bees’ flower visits is illustrated.

A new algorithm of generating a minimum spanning tree of a graph based on recursion

2014 ◽  
Author(s):  
Meiqin Pan ◽  
Zhijun Ding
Keyword(s):  
Spanning Tree ◽  
Minimum Spanning Tree

Pendekatan Matriks Ketetanggaan Berbobot untuk Solusi Minimum Spanning Tree (MST)

2020 ◽  
Vol 4 (3) ◽  
pp. 280
Author(s):  
Tri Yani Akhirina ◽  
Thomas Afrizal
Keyword(s):  
Spanning Tree ◽  
Minimum Spanning Tree

scholarly journals Penalty Strategy in The Fitness Function of Grey Wolf Optimizer for Minimum Spanning Tree Problem

2021 ◽  
Vol 1077 (1) ◽  
pp. 012071
Author(s):  
Z Zukhri ◽  
I V Paputungan ◽  
N S Handayani ◽  
V Satriadi
Keyword(s):  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Fitness Function ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Minimum Spanning Tree Problem ◽  
Penalty Strategy

scholarly journals Converting MST to TSP Path by Branch Elimination

2020 ◽  
Vol 11 (1) ◽  
pp. 177
Author(s):  
Pasi Fränti ◽  
Teemu Nenonen ◽  
Mingchuan Yuan
Keyword(s):  
Spanning Tree ◽  
Closed Loop ◽  
Minimum Spanning Tree ◽  
Travelling Salesman Problem ◽  
Open Loop ◽  
Travelling Salesman ◽  
Elimination Algorithm ◽  
Number Of Iterations ◽  
Number Of Branches

Travelling salesman problem (TSP) has been widely studied for the classical closed loop variant but less attention has been paid to the open loop variant. Open loop solution has property of being also a spanning tree, although not necessarily the minimum spanning tree (MST). In this paper, we present a simple branch elimination algorithm that removes the branches from MST by cutting one link and then reconnecting the resulting subtrees via selected leaf nodes. The number of iterations equals to the number of branches (b) in the MST. Typically, b << n where n is the number of nodes. With O-Mopsi and Dots datasets, the algorithm reaches gap of 1.69% and 0.61 %, respectively. The algorithm is suitable especially for educational purposes by showing the connection between MST and TSP, but it can also serve as a quick approximation for more complex metaheuristics whose efficiency relies on quality of the initial solution.

Latency, Capacity, and Distributed Minimum Spanning Tree†

2020 ◽  
Author(s):  
John Augustine ◽  
Seth Gilbert ◽  
Fabian Kuhn ◽  
Peter Robinson ◽  
Suman Sourav
Keyword(s):  
Spanning Tree ◽  
Minimum Spanning Tree

DEVELOPING PROXIMITY GRAPHS BY PHYSARUM POLYCEPHALUM: DOES THE PLASMODIUM FOLLOW THE TOUSSAINT HIERARCHY?

2009 ◽  
Vol 19 (01) ◽  
pp. 105-127 ◽  
Author(s):  
ANDREW ADAMATZKY
Keyword(s):  
Foraging Behavior ◽  
Delaunay Triangulation ◽  
Spanning Tree ◽  
Physarum Polycephalum ◽  
Minimum Spanning Tree ◽  
Nearest Neighbour ◽  
Minimal Spanning Tree ◽  
Proximity Graphs ◽  
Relative Neighborhood Graph ◽  
Plasmodium Of Physarum Polycephalum

Plasmodium of Physarum polycephalum spans sources of nutrients and constructs varieties of protoplasmic networks during its foraging behavior. When the plasmodium is placed on a substrate populated with sources of nutrients, it spans the sources with protoplasmic network. The plasmodium optimizes the network to deliver efficiently the nutrients to all parts of its body. How exactly does the protoplasmic network unfold during the plasmodium's foraging behavior? What types of proximity graphs are approximated by the network? Does the plasmodium construct a minimal spanning tree first and then add additional protoplasmic veins to increase reliability and through-capacity of the network? We analyze a possibility that the plasmodium constructs a series of proximity graphs: nearest-neighbour graph (NNG), minimum spanning tree (MST), relative neighborhood graph (RNG), Gabriel graph (GG) and Delaunay triangulation (DT). The graphs can be arranged in the inclusion hierarchy (Toussaint hierarchy): NNG ⊆ MST ⊆ RNG ⊆ GG ⊆ DT . We aim to verify if graphs, where nodes are sources of nutrients and edges are protoplasmic tubes, appear in the development of the plasmodium in the order NNG → MST → RNG → GG → DT , corresponding to inclusion of the proximity graphs.

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