A Road Network Construction Method Based on Clustering Algorithm

This paper presents a novel methodology for Climate Network (CN) construction based on the Kullback-Leibler divergence (KLD) among Membership Probability (MP) distributions, obtained from the Second Order Data-Coupled Clustering (SODCC) algorithm. The proposed method is able to obtain CNs with emergent behaviour adapted to the variables being analyzed, and with a low number of spurious or missing links. We evaluate the proposed method in a problem of CN construction to assess differences in wind speed prediction at different wind farms in Spain. The considered problem presents strong local and mesoscale relationships, but low synoptic scale relationships, which have a direct influence in the CN obtained. We carry out a comparison of the proposed approach with a classical correlation-based CN construction method. We show that the proposed approach based on the SODCC algorithm and the KLD constructs CNs with an emergent behaviour according to underlying wind speed prediction data physics, unlike the correlation-based method that produces spurious and missing links. Furthermore, it is shown that the climate network construction method facilitates the evaluation of symmetry properties in the resulting complex networks.

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A Novel High-Dimensional Trajectories Construction Network based on Multi-Clustering Algorithm

10.21203/rs.3.rs-1060086/v1 ◽

2021 ◽

Author(s):

Feiyang Ren ◽

Yi Han ◽

Shaohan Wang ◽

He Jiang

Keyword(s):

Economic Analysis ◽

Clustering Algorithm ◽

Transportation Network ◽

High Dimensional ◽

Clustering Methods ◽

Marine Transportation ◽

Network Construction ◽

National Economic ◽

Multi Level ◽

State Of Art

Abstract A novel marine transportation network based on high-dimensional AIS data with a multi-level clustering algorithm is proposed to discover important waypoints in trajectories based on selected navigation features. This network contains two parts: the calculation of major nodes with CLIQUE and BIRCH clustering methods and navigation network construction with edge construction theory. Unlike the state-of-art work for navigation clustering with only ship coordinate, the proposed method contains more high-dimensional features such as drafting, weather, and fuel consumption. By comparing the historical AIS data, more than 220,133 lines of data in 30 days were used to extract 440 major nodal points in less than 4 minutes with ordinary PC specs (i5 processer). The proposed method can be performed on more dimensional data for better ship path planning or even national economic analysis. Current work has shown good performance on complex ship trajectories distinction and great potential for future shipping transportation market analytical predictions.

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A functional network construction method to interpret the pathological process of colorectal cancer

International Journal of Data Mining and Bioinformatics ◽

10.1504/ijdmb.2020.107879 ◽

2020 ◽

Vol 23 (3) ◽

pp. 251

Author(s):

Bolin Chen ◽

Manting Yang ◽

Li Gao ◽

Tao Jiang ◽

Xuequn Shang

Keyword(s):

Colorectal Cancer ◽

Pathological Process ◽

Construction Method ◽

Functional Network ◽

Network Construction

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D3GRN: a data driven dynamic network construction method to infer gene regulatory networks

BMC Genomics ◽

10.1186/s12864-019-6298-5 ◽

2019 ◽

Vol 20 (S13) ◽

Cited By ~ 1

Author(s):

Xiang Chen ◽

Min Li ◽

Ruiqing Zheng ◽

Fang-Xiang Wu ◽

Jianxin Wang

Keyword(s):

Gene Regulatory Networks ◽

Regulatory Networks ◽

Dynamic Network ◽

Construction Method ◽

Data Driven ◽

Regression Problem ◽

Network Construction ◽

Benchmark Datasets ◽

Gene Regulatory ◽

New Perspective

Abstract Background To infer gene regulatory networks (GRNs) from gene-expression data is still a fundamental and challenging problem in systems biology. Several existing algorithms formulate GRNs inference as a regression problem and obtain the network with an ensemble strategy. Recent studies on data driven dynamic network construction provide us a new perspective to solve the regression problem. Results In this study, we propose a data driven dynamic network construction method to infer gene regulatory network (D3GRN), which transforms the regulatory relationship of each target gene into functional decomposition problem and solves each sub problem by using the Algorithm for Revealing Network Interactions (ARNI). To remedy the limitation of ARNI in constructing networks solely from the unit level, a bootstrapping and area based scoring method is taken to infer the final network. On DREAM4 and DREAM5 benchmark datasets, D3GRN performs competitively with the state-of-the-art algorithms in terms of AUPR. Conclusions We have proposed a novel data driven dynamic network construction method by combining ARNI with bootstrapping and area based scoring strategy. The proposed method performs well on the benchmark datasets, contributing as a competitive method to infer gene regulatory networks in a new perspective.

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