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clustering tropical cyclone genesis on ENSO timescales in the Southwest Pacific

CLIMATE DYNAMICS 2023年第11-12期60卷 3353-3368页

作者： Tu'uholoaki, Moleni Espejo, Antonio Singh, Awnesh Damlamian, Herve Wandres, Moritz Chand, Savin Mendez, Fernando J. Fa'anunu, Ofa Univ South Pacific Pacific Environm & Sustainable Dev Suva Fiji Pacific Community Geosci Energy & Maritime Div Suva Fiji Tonga Meteorol Serv Nuku Alofa Tonga Univ Cantabria Dept Sci & Tech Water & Environm Santander Spain Federat Univ Inst Innovat Sci & Sustainabil Univ Dr Ballarat Vic 3350 Australia

Tropical cyclones (TCs) as a natural hazard pose a major threat and risk to the human population globally. This threat is expected to increase in a warming climate as the frequency of severe TCs is expected to increase. In this study, the influence of different monthly sea surface temperature (SST) patterns on the locations and frequency of tropical cyclone genesis (TCG) in the Southwest Pacific (SWP) region is investigated. Using principal component analysis and k-means clustering of monthly SST between 1970 and 2019, nine statistically different SST patterns are identified. Our findings show that the more prominent ENSO patterns such as the Modoki El Nino (i.e., Modoki I and Modoki II) and Eastern Pacific (EP) El Nino impact the frequency and location of TCG significantly. Our results enhance the overall understanding of the TCG variability and the relationship between TCG and SST configurations in the SWP region. The results of this study may support early warning system in SWP by improving seasonal outlooks and quantification of the level of TC-related risks for the vulnerable Pacific Island communities.

关键词： ENSO types Sea surface temperatures k-means clustering algorithm Principal component analysis Tropical cyclones

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Factor Modeling for clustering High-Dimensional Time Series

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JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 2024年第546期119卷 1252-1263页

作者： Zhang, Bo Pan, Guangming Yao, Qiwei Zhou, Wang Univ Sci & Technol China Int Inst Finance Sch Management Dept Stat & Finance Hefei Peoples R China Nanyang Technol Univ Sch Phys & Math Sci Singapore Singapore London Sch Econ & Polit Sci Dept Stat London England Natl Univ Singapore Dept Stat & Data Sci Singapore Singapore

We propose a new unsupervised learning method for clustering a large number of time series based on a latent factor structure. Each cluster is characterized by its own cluster-specific factors in addition to some common factors which impact on all the time series concerned. Our setting also offers the flexibility that some time series may not belong to any clusters. The consistency with explicit convergence rates is established for the estimation of the common factors, the cluster-specific factors, and the latent clusters. Numerical illustration with both simulated data as well as a real data example is also reported. As a spin-off, the proposed new approach also advances significantly the statistical inference for the factor model of Lam and Yao. for this article are available online.

关键词： Eigenanalysis Idiosyncratic components k-means clustering algorithm Strong and weak factors

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Adaptive POD surrogate model method for centrifugal pump impeller flow field reconstruction based on clustering algorithm

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MODERN PHYSICS LETTERS B 2021年第7期35卷

作者： Chen, Xuebing Zhang, Renhui Jiang, Lijie Yang, Weifeng Lanzhou Univ Technol Coll Energy & Power Engn Lanzhou 730050 Gansu Peoples R China Key Lab Fluid Machinery & Syst Lanzhou 730050 Gansu Peoples R China

To reduce the calculation cost and improve the accuracy of flow field prediction, an adaptive proper orthogonal decomposition (APOD) surrogate model based on k-means clustering algorithm was proposed to reconstruct the flow field of impeller. The experiment samples were designed by introducing the perturbation of the blade control parameters such as blade wrap angle and blade angle of outlet. k-means clustering algorithm was used to classify the sample blade shapes, and find out the cluster of the objective blade. The snapshot set, which consisted of the blade shape and the flow field data of impeller, can be described as a linear combination of orthogonal basis by POD method. The radial basis function (RBF) was used to fit the orthogonal basis coefficients of the objective blade, and then the flow field of objective impeller was reconstructed. The traditional fixed sample POD (FPOD) method and the proposed APOD method were used to reconstruct the flow field in impeller, respectively, and the prediction results of the two methods were compared and analyzed. The results show that the proposed APOD method could quickly and accurately reconstruct the objective flow field. The flow field prediction accuracy of the APOD method is significantly higher than the FPOD method, and the calculation time for the flow field prediction is less than 1/360 of the CFD.

关键词： Centrifugal pump impeller surrogate model proper orthogonal decomposition adaptive k-means clustering algorithm

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Comparison of clustering algorithms on generalized propensity score in observational studies: a simulation study

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JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 2013年第12期83卷 2206-2218页

作者： Tu, Chunhao Jiao, Shuo koh, Woon Yuen Univ New England Coll Pharm Portland ME 04103 USA Fred Hutchinson Canc Res Ctr Div Publ Hlth Seattle WA 98109 USA Univ New England Dept Math Sci Biddeford ME 04005 USA

In observational studies, unbalanced observed covariates between treatment groups often cause biased inferences on the estimation of treatment effects. Recently, generalized propensity score (GPS) has been proposed to overcome this problem;however, a practical technique to apply the GPS is lacking. This study demonstrates how clustering algorithms can be used to group similar subjects based on transformed GPS. We compare four popular clustering algorithms: k-means clustering (kMC), model-based clustering, fuzzy c-means clustering and partitioning around medoids based on the following three criteria: average dissimilarity between subjects within clusters, average Dunn index and average silhouette width under four various covariate scenarios. Simulation studies show that the kMC algorithm has overall better performance compared with the other three clustering algorithms. Therefore, we recommend using the kMC algorithm to group similar subjects based on the transformed GPS.

关键词： fuzzy c-means clustering algorithm generalized propensity score k-means clustering algorithm model-based clustering algorithm observational studies partitioning around medoids algorithm

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An efficient method for extracting and clustering rock mass discontinuities from 3D point clouds

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ACTA GEOTECHNICA 2023年第7期18卷 3485-3503页

作者： Yi, Xiaoyu Feng, Wenkai Wang, Di Yang, Ronghao Hu, Yunpeng Zhou, Yongjian Chengdu Univ Technol State Key Lab Geohazard Prevent & Geoenvironm Prot Chengdu 610059 Peoples R China Chengdu Univ Technol Coll Environm & Civil Engn Chengdu 610059 Peoples R China Chengdu Univ Technol Coll Earth Sci Chengdu 610059 Peoples R China

A rock mass discontinuity is a fundamental element of a rock mass structure that regulates its mechanical and hydrogeological properties and has significant engineering implications. In this research, we offer a method for extracting discontinuities and performing efficient clustering analysis based on 3D point cloud data for rock outcrops. First, the k-d tree approach is utilized to organize the point cloud data so that the normal vector and curvature can be calculated quickly. Discontinuities are then extracted using a multirule region growing algorithm, and the dip directions and dip angles of the discontinuities are calculated. Then, the improved farthest point sampling algorithm and the elbow method are used to optimize the k-means algorithm and finally automatically determine the main discontinuity set and average direction for the rock mass. The approach is tested on two real cases and compared to the methods of international researchers, and it is discovered that the method proposed in this work shows good accuracy, with an average deviation of less than 5 degrees from the dip direction and dip angle. Comparative tests with many point cloud data sets show that this new method can be used to extract discontinuities from massive-scale rock outcrop point cloud data and perform cluster analysis with high efficiency. The proposed method gives geologists and geological engineers a new tool for quickly and efficiently understanding rock outcrop discontinuities.

关键词： 3D point cloud Region growing algorithm Rock mass discontinuity k-means clustering algorithm

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An extended k-means technique for clustering moving objects

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EGYPTIAN INFORMATICS JOURNAL 2011年第1期12卷 45-51页

作者： Ossama, Omnia Mokhtar, Hoda M. O. El-Sharkawi, Mohamed E. Cairo Univ Fac Comp & Informat Cairo Egypt

k-means algorithm is one of the basic clustering techniques that is used in many data mining applications. In this paper we present a novel pattern based clustering algorithm that extends the k-means algorithm for clustering moving object trajectory data. The proposed algorithm uses a key feature of moving object trajectories namely, its direction as a heuristic to determine the different number of clusters for the k-means algorithm. In addition, we use the silhouette coefficient as a measure for the quality of our proposed approach. Finally, we present experimental results on both real and synthetic data that show the performance and accuracy of our proposed technique. (C) 2011 Faculty of Computers and Information, Cairo University. Production and hosting by Elsevier B. V. All rights reserved.

关键词： clustering moving objects Moving objects databases Mining moving object trajectories k-means clustering algorithm

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A k-means clustering Based Message Forwarding Model for Internet of Things (IoT) 8

A K-Means Clustering Based Message Forwarding Model for Inte...

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8th IEEE International Conference on Cloud Computing, Data Science and Engineering (Confluence) / Global Technology, Innovation and Enterpreneurship Summit

作者： kumar, Sumit Raza, Zahid Jawaharlal Nehru Univ Sch Comp & Syst Sci Newdelhi India

ISBN: (纸本)9781538617199

Internet of Things (IoT) is establishing to be the next stage of Internet resulting in a relation between internet connection data generating smart objects. IoT is understood as a seamless interconnection of sensor objects with the computational world. clustering these objects is very helpful for data analytics generating a combination of structure of similar types of object characteristics. clustering, therefore, is very useful in IoT for improving the Quality of Service (QoS) and organizing the resources. In this work, a k-means clustering based resource allocation model for IoT has been discussed. The performance has been evaluated primarily for the response time realized for transmission of a message from the source to the destination. Simulation study reveals the effectiveness of the model.

关键词： Internet of Things (IoT) clustering k-means clustering algorithm Response time

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Rule Reduction for EBRB Classification Based on clustering 1

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18th Web Information Systems and Applications Conference (WISA)

作者： Chen, Longjiang Fu, Yanggeng Chen, Nannan Ye, Jifeng Liu, Genggeng Fuzhou Univ Coll Math & Comp Sci Fuzhou Peoples R China

ISBN: (数字)9783030875718

ISBN: (纸本)9783030875718;9783030875701

The extended belief rule base (EBRB) system has been successfully applied to classification problems in various fields. However, the existing EBRB generation method converts all data into extended belief rules, which leads to the large scale of rule base and affects the efficiency and accuracy of subsequent inference. In view of this, this paper proposes an EBRB rule reduction method based on the adaptive k-means clustering algorithm (RC-EBRB). In the rule generation process, the k-means clustering algorithm is applied to obtain the rule cluster centers, which are used to generate new rules. In the end, these new rules form a reduced EBRB. Moreover, in order to determine the initial cluster centers and the number of clusters in the k-means clustering algorithm, the algorithm idea of k-means++ is introduced and a reduction granularity adjustment algorithm with threshold is proposed, respectively. Finally, four datasets on commonly used classification datasets from UCI are used to verify the performance of the proposed method. The experimental results are compared with the existing EBRB methods and the traditional machine learning methods, which prove the effectiveness of the method.

关键词： Extended belief rule base Rule reduction Data-driven k-means clustering algorithm

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Electricity Information Big Data Based Load Curve clustering 6

Electricity Information Big Data Based Load Curve Clustering

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China International Conference on Electricity Distribution (CICED)

作者： Zheng, Haiyan Jin, Nong Xiong, Zheng Ji, Cong Fang, Chao Zhong, Chunlin Jiangsu Frontier Elect Technol CO LTD Nanjing 211102 Jiangsu Peoples R China Jiangsu Elect Power Co Nanjing 210024 Jiangsu Peoples R China

ISBN: (纸本)9781479941261

clustering analysis of load curves on basis of electricity information big data is an important basis of load characterisitic and electricity consumption habits analysis of large users. In view of the slow speed of traditional k-means clustering algorithm in the background of big data, a parallel k-means clustering algorithm is proposed to speed up the clustering procedure. Firstly, all the load curves are de-noised by wavelet decomposing in order to reduce the influence of small fluctuations. Secondly, a multi-core parallel technology based k-means clustering algorithm is applied to load curve clustering. Thirdly, more than 40,000 load curves are clustered by the multi-core parallel technology based k-means clustering algorithm. Test results show that the proposed parallel k-means clustering algorithm can speed up clustering procedure effectively.

关键词： big data wavelet denoising load curve cluster k-means clustering algorithm multi-core paralleltechnology

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Outdoor Location Fingerprint Based on Optimized k-means clustering

Outdoor Location Fingerprint Based on Optimized K-means Clus...

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IEEE/CIC International Conference on Communications in China (ICCC)

作者： Liu, Jingjing Chuai, Gang Gao, Weidong Beijing Univ Posts & Telecommun Key Lab Universal Wireless Commun Minist Educ Beijing Peoples R China

ISBN: (纸本)9781665439442

In the indoor scene, location fingerprint has been developed to improve the locating speed by using clustering algorithm to process the fingerprint database. In the outdoor scene, location fingerprint is still at the stage of constructing a fingerprint database due to the vast region and its large data, so its locating speed needs to be faster. In the view of the slow speed, this paper proposes optimized k-means algorithm by I-CFSFDP algorithm to process the outdoor fingerprint database, aiming to greatly increase the locating speed without sacrificing the accuracy of locating. In order to verify the performance of the algorithm, the standard propagation model is applied to calculate the sampling point's reference signal received power (RSRP), and universal kriging algorithm is used to interpolate the database, ensuring the authenticity of outdoor environment simulation. The result shows that the locating speed of outdoor fingerprint database can be greatly improved without decreasing accuracy, after being processed by I-CFSFDP optimized k-means.

关键词： outdoor locating location fingerprint RSRP universal kriging interpolation algorithm k-means clustering algorithm

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