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2nd International Conference on Data Science and Business Analytics (ICDSBA)

作者： Yu, Lianmin Zhou, Changyin Shandong Univ Sci & Technol Coll Math & Syst Sci Qingdao Shandong Peoples R China

ISBN: (纸本)9781538684313

In this paper, the selection of clustering number in k-means clustering algorithm is studied, based on the Bootstrap sampling, a new method is proposed to determine the best clustering number based on the between the actual value of total within-cluster sum of squares and its estimated interval. By UCI Machine Learning Repository and randomly generated artificial simulated test data sets, the experimental results show that using clustering has obvious improvement, the method can overcome falls into local optimum caused by unreasonable clustering number to select. k-means algorithm.

关键词： k-means clustering algorithm the best clustering number within-cluster sum of squares Bootstrap sampling

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New clustering Techniques of Node Embeddings Based on Metaheuristic Optimization algorithms 13th

New Clustering Techniques of Node Embeddings Based on Metahe...

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13th International Conference on Large-Scale ScientificComputations (LSSC)

作者： Alihodzic, Adis Chahin, Malek Cunjalo, Fikret Univ Sarajevo Dept Math Zmaja Bosne 33-35 Sarajevo 71000 Bosnia & Herceg

ISBN: (纸本)9783030975494;9783030975487

Node embeddings present a powerful method of embedding graph-structured data into a low dimensional space while preserving local node information. clustering is a common preprocessing task on unsupervised data utilized to get the best insight into the input dataset. The most prominent clustering algorithm is the k-means algorithm. In this paper, we formulate clustering as an optimization problem using different objective functions following the idea of searching for the best fit centroid-based cluster exemplars. We also apply several nature-inspired optimization algorithms since the k-means algorithm is trapped in local optima during its execution. We demonstrate our cluster frameworks' capability on several graph clustering datasets used in node embeddings and node clustering tasks. Performance evaluation and comparison of our frameworks with the k-means algorithm are demonstrated and discussed in detail. We end this paper with a discussion on the impact of the objective function's choice on the clustering results.

关键词： Graph embedding Node embeddings clustering Community detection Metaheuristics k-means clustering algorithm

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Trusted Power Load Management Communication Framework Using Topology clustering and Blockchain Authentication 5

Trusted Power Load Management Communication Framework Using ...

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5th International Conference on Mechatronics Technology and Intelligent Manufacturing (ICMTIM)

作者： Chen, ke Liu, Yinghui Zhuang, Zhong Su, Huiling Duan, Meimei China Elect Power Res Inst Beijing Key Lab Demand Side Multienergy Carriers Beijing Peoples R China State Grid Jiangsu Mkt Serv Ctr Metrol Ctr Nanjing Peoples R China

ISBN: (纸本)9798350363272;9798350363265

This paper proposes a novel communication framework for a new type of power load management system, utilizing a geographic clustering approach based on the k-means algorithm to optimize network topology. Each cluster of communication nodes forms a subnet, with the cluster center serving as a routing node. Leveraging blockchain principles, each subnet is structured as a circular blockchain, starting with the routing node as the genesis block. This node initializes communication with a timestamp and computes the hash of the block, passing it to the next node. Subsequent nodes add their data and timestamps to the previous hash, computing new hashes and continuing the chain. The last node in the subnet links back to the routing node, forming a closed loop, which enables the routing node to validate the integrity of the entire subnet's data. The same validation mechanism is extended between the routing nodes and the main station of the power load management system, ensuring robust and trustworthy communication across the network. This dual verification mechanism enhances the reliability and security of data transmission within the power load management system.

关键词： power load management systems network topology k-means clustering algorithm communication framework trustworthy communication

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Comparison of Business Environments in Oil-Rich MENA Countries: A clustering Analysis of Economic Diversification and Performance

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EMERGING MARkETS FINANCE AND TRADE 2019年第12期55卷 2871-2885页

作者： Aker, Sule L. Aghaei, Iman Eastern Mediterranean Univ Dept Business Adm Mersin 10 Famagusta Turkey

The purpose of present research is to cluster 11 oil-richest MENA countries;Algeria, Egypt, Iran, Iraq, kuwait, Libya, Oman, Qatar, Saudi Arabia, Sudan, and the United Arab Emirates regarding economic diversification and performance during 2010-2016 using k-means algorithm which is a versatile data mining technique. The major finding is that higher export diversity in addition to better economic performance can influence competitiveness of business environment of the countries positively and vice versa. The studied nations are partitioned into four clusters. Qatar and UAE with highest economic diversity and performance have the most favorable business environments in the region.

关键词： Business environment economic diversification economic performance k-means clustering algorithm MENA countries

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Study on urban road network traffic district division based on clustering analysis

Study on urban road network traffic district division based ...

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Chinese Automation Congress (CAC)

作者： Zhao, Luhua Li, Yang Shandong Univ Sci & Technol Coll Transportat Qingdao Peoples R China

ISBN: (纸本)9781728113128

Aiming at the problems of road congestion and complex road network information during peak commuting period, a spatial statistical method for traffic district division based on k-means algorithm is proposed. This method uses Jinan taxi GPS data, uses Python software to realize the k-means clustering division of taxi departure and departure points, and uses Moran's I model to analyze the spatial statistical characteristics of the partition results. Then, the taxi speed data is analyzed by ArcGIS software to further divide the community according to the clustering results. The results w ill he displayed on the map. The results show that this method can more reasonably and accurately classify traffic districts by collecting and analyzing a large number of historical data

关键词： traffic area k-means clustering algorithm Python spatial statistics ArcGIS

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A Low-Voltage Distribution Network Line Loss Calculation Method Based on Enhanced k-means clustering and BP Neural Network 3

A Low-Voltage Distribution Network Line Loss Calculation Met...

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3rd International Conference on Electrical Engineering and Control Science, IC2ECS 2023

作者： Cong, Rili Man, Xiangyang Wang, Xiaoyan Fan, Jiaqi Liu, Jianfeng Economic and Technological Research Institute State Grid Inner Mongolia Eastern Electric Power Co. Ltd. Hohhot010020 China

ISBN: (纸本)9798350382426

As the energy revolution progresses and efforts to achieve the 'double carbon' target continue, the calculation of distribution network line losses as an important technical indicator will effectively reflect the economic stability and operational capacity of the network. This paper presents an effective approach to computing the wire loss in photovoltaic integrated low-voltage electrical substation systems, addressing issues such as precision and pace of computation. First, the wire loss indicators presented in this article are extracted using hierarchical analysis, and the enhanced k-means clustering method is then used to classify the wire loss data in accordance with the resulting metrics. Subsequently, a BP neural network optimized by LM is applied for the classification and training of the model data, and the correlation is fitted between the wire loss rate and exponent. Simulation calculations using sample data from a PV-integrated low-voltage distribution network in a region are undertaken to prove the feasibility of the proposal. © 2023 IEEE.

关键词： BP network k-means clustering algorithm low-voltage region power line loss

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POD-DEIM model order reduction for strain-softening viscoplasticity

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2017年 317卷 458-479页

作者： Ghavamian, F. Tiso, P. Simone, A. Delft Univ Technol Fac Civil Engn & Geosci POB 5048 NL-2600 GA Delft Netherlands Swiss Fed Inst Technol Inst Mech Syst Leonhardstr 21 CH-8092 Zurich Switzerland

We demonstrate a Model Order Reduction technique for a system of nonlinear equations arising from the Finite Element Method (FEM) discretization of the three-dimensional quasistatic equilibrium equation equipped with a Perzyna viscoplasticity constitutive model. The procedure employs the Proper Orthogonal Decomposition-Galerkin (POD-G) in conjunction with the Discrete Empirical Interpolation Method (DEIM). For this purpose, we collect samples from a standard full order FEM analysis in the offline phase and cluster them using a novel k-means clustering algorithm. The POD and the DEIM algorithms are then employed to construct a corresponding reduced order model. In the online phase, a sample from the current state of the system is passed, at each time step, to a nearest neighbor classifier in which the cluster that best describes it is identified. The force vector and its derivative with respect to the displacement vector are approximated using DEIM, and the system of nonlinear equations is projected onto a lower dimensional subspace using the POD-G. The constructed reduced order model is applied to two typical solid mechanics problems showing strain-localization (a tensile bar and a wall under compression) and a three-dimensional squarefooting problem. (C) 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.

关键词： Model order reduction Proper orthogonal decomposition Discrete empirical interpolation method Perzyna viscoplasticity Strain-softening Machine learning k-means clustering algorithm

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Principal component analysis-based learning for preceding vehicle classification

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IET INTELLIGENT TRANSPORT SYSTEMS 2014年第1期8卷 28-35页

作者： Mangai, Muthulingam Alarmel Gounden, Nanjappagounder Ammasai Natl Inst Technol Dept Elect & Elect Engn Tiruchirappalli Tamil Nadu India

This study presents a new scheme for cluster generation and classification of preceding vehicles from images. The proposed clustering algorithm models the distribution of vehicle images using vehicle' clusters. Non-vehicle' clusters are generated by modelling the distribution of non-vehicle images. The clusters are created using k-means clustering algorithm. Hierarchically related nested eigenspaces are acquired to reassign the patterns of each cluster. An appropriate classifier is obtained to classify the vehicles based on the distance-from-feature-space' measurement. The eigenspaces of vehicle clusters together with non-vehicle clusters are used for classification. The approach of modelling the distribution of vehicle and non-vehicle images and the choice of the classifier used are investigated through experiments thoroughly. Comparison on the performance of the proposed scheme is made with that of MultiClustered Modified Quadratic Discriminant Function approach of categorising the preceding vehicles. The superior performance of the proposed scheme is clearly illustrated through the classification results.

关键词： image classification learning (artificial intelligence) pattern clustering principal component analysis traffic engineering computing principal component analysis-based learning preceding vehicle classification cluster generation vehicle image distribution nonvehicle clusters k-means clustering algorithm nested eigenspaces classifier distance-from-feature-space measurement multiclustered modified quadratic discriminant function approach

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Frequency-hopping signals sorting based on underdetermined blind source separation

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IET COMMUNICATIONS 2013年第14期7卷 1456-1464页

作者： Sha, Zhi-Chao Huang, Zhi-Tao Zhou, Yi-Yu Wang, Feng-Hua Natl Univ Def Technol Coll Elect Sci & Engn Changsha 410073 Hunan Peoples R China

Most of the earlier approaches for frequency-hopping (FH) signals sorting with a single sensor do not adapt to synchronous network, whereas the multiple-sensor-based algorithms request the number of sensors must be more than that of signals. Since the number of sensors is limited in many practical applications, it is important to sort synchronous or asynchronous FH networks with as little as possible sensors. This study introduces the underdetermined blind source separation (UBSS) algorithm to solve this problem. Considering the time-frequency (TF) sparsity of FH signals, the problem is formulated as one of UBSS based on sparse TF representation. First, an improved k-means clustering algorithm is developed to estimate the mixing matrix, according to the TF properties of FH signals. Secondly, to separate more signals overlapped in the TF domain using given sensors, an improved subspace-based algorithm utilising the information of the comparative power is proposed. In the proposed method, the sparsity condition of the sources in the TF domain is relaxed, and the number of FH signals that exist at any TF point simultaneously is allowed to equal that of the sensors. Simulations demonstrate that the proposed method can separate FH signals efficiently and outperforms the previous methods.

关键词： blind source separation estimation theory frequency hop communication frequency hopping signal underdetermined blind source separation UBSS algorithm time-frequency sparsity sparse time-frequency representation k-means clustering algorithm mixing matrix estimation comparative power source sparsity condition time-frequency domain time-frequency point

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Has the public habituated to the haze in China?

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2022年第15期29卷 21396-21411页

作者： Wang, Xinxin Chen, Nan Shi, Xingmin Shaanxi Normal Univ Sch Geog & Tourism Xian 710119 Peoples R China

The concept of haze habituation was proposed based on haze perception and behavior in this paper. This study employed factor analysis and Potential Conflict Index (PCI) to analyze the dimensions, degrees, and internal differences of the public's haze habituation. Then, k-means clustering algorithm was applied to classify the public into four categories. The entropy method was used to quantitatively evaluate the public's haze habituation, and the natural breakpoint method was used to grade it into five levels. Finally, an ordered logistic regression model was chosen to analyze the influencing factors of the public's haze habituation. The results indicate that: (1) The public's haze habituation can be measured from five dimensions: protective behavior, haze reduction behavior, haze attention, life impact perception, and health impact perception. The public had the same views on protective behavior, haze reduction behavior, life impact perception, and health impact perception. However, there is a wide divergence among the public on the haze attention;(2) Based on the above five dimensions, the public can be divided into the protective sensitive group, attention sensitive group, health sensitive group, and environmental protection sensitive group;(3) Generally, the public has a low haze habituation where the protective behavior, haze reduction behavior, and health impact perception are the crucial elements;(4) Gender, self-health assessment, and travel mode have a significant positive impact on the public's haze habituation, respectively. Age, the family with elders or children, and annual family income have a significant negative impact on the public's haze habituation, respectively.

关键词： Haze Perception Potential Conflict Index (PCI) k-means clustering algorithm Natural breakpoint method China

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