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A clustering algorithm using skewness-based boundary detection

NEUROCOMPUTING 2018年 275卷 618-626页

作者： Li, Xiangli Han, Qiong Qiu, Baozhi Zhengzhou Univ Sch Informat Engn Zhengzhou 450001 Henan Peoples R China

clustering analysis has been applied in all aspects of data mining. Density-based and grid-based clustering algorithms are used to form clusters from the core points or dense grids to extend to the boundary of the clusters. However, deficiencies are still existed. To find out the right boundary and improve the precision of the cluster, this paper has proposed a new clustering algorithm (named C-USB) based on the skew characteristic of the data distribution in the cluster margin region. The boundary degree calculated by skew degree and the local density are used to distinguish whether a data is an internal point or non-internal point. And the connected matrix is constructed by removing the neighbor relationships of non-internal points from the relationships of all points, then the clusters can be formed by searching from the connected matrix towards internal of the clusters. Experimental results on synthetic and real data sets show that the C-USB has higher accuracy than that of similar algorithms. (C) 2017 Published by Elsevier B.V.

关键词： Skewness Boundary degree clustering algorithm clustering boundary

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Customer segmentation research in marketing through clustering algorithm analysis

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第4期45卷 5441-5448页

作者： Zheng, Yulan Fuzhou Univ Coll Econ & Management Fuzhou 350108 Fujian Peoples R China

In marketing, customer segmentation is a very critical element. This paper focuses on clustering algorithms. First, the commonly used K-means algorithm was introduced, and then, it was optimized using the improved Lion Swarm Optimization (ILSO) algorithm and the Calinski-Harabasz (CH) index. The results of the experiment for the UCI dataset showed that the CH indicator obtained an accurate number of clusters, and the clustering accuracy of the ILSO-K-means algorithm was higher, both above 90%. Then, in customer segmentation, the customers of an enterprise were divided into four groups using the ILSO-K-means algorithm, and different marketing suggestions were given. The experimental analysis proves the usability of the ILSO-K-means algorithm in customer segmentation, which can be further applied in practice.

关键词： clustering algorithm marketing customer segmentation lion cluster optimization algorithm marketing methods

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A novel clustering algorithm for grouping and cascade utilization of retired Li-ion batteries

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JOURNAL OF ENERGY STORAGE 2020年 29卷

作者： Xu Zhicheng Jun, Wang Peter, Lund D. Qi, Fan Ting, Dong Yan, Liang Jie, Hong Southeast Univ Key Lab Solar Energy Sci & Technol Jiangsu Prov 2 Si Pai Lou Nanjing 210096 Peoples R China Southeast Univ Energy Storage Res Ctr 2 Si Pai Lou Nanjing 210096 Peoples R China Aalto Univ Sch Sci POB 15100 FI-00076 Espoo Finland Jiangsu New Energy Dev Co Ltd 88 Changjiang Rd Nanjing 210005 Peoples R China

The rapid deployment of lithium-ion batteries in clean energy and electric vehicle applications will also increase the volume of retired batteries in the coming years. Retired Li-ion batteries could have residual capacities up to 70-80% of the nominal capacity of a new battery, which could be lucrative for a second-life battery market, also creating environmental and economic benefits. Presently, retired batteries are first screened to select usable batteries and then a proper secondary application is choosen according to the battery performance. Here, a complete process for grouping used batteries is proposed including safety checking, performance evaluation, data processing, and clustering of batteries. Also, a novel clustering algorithm of retired batteries based on traversal optimization is proposed. The new method does not require defining the cluster numbers and centers in beforehand, but possesses immunity to outliers. It can be used both for small and large sample sizes, as the optimization parameters used do not require iteration. The Davies-Bouldin Index of the proposed algorithm shows that the greatest differences are found between clusters, but the least differences between the samples within a single cluster, which indicates the effectiveness of the algorithm.

关键词： Retired Li-ion batteries Battery grouping clustering algorithm Davies-Bouldin Index

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A Novel Possibilistic clustering algorithm for Measurement Data of Vehicle MMW Radar

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IEEE SENSORS JOURNAL 2023年第15期23卷 17103-17116页

作者： Wang, Tao Wu, Shu Cao, Lin Du, Kangning Zhao, Zongmin He, Zhenxue Beijing Informat Sci & Technol Univ Sch Informat & Commun Engn Beijing 100101 Peoples R China Beijing Informat Sci & Technol Univ Key Lab Minist Educ Optoelect Measurement Technol Beijing 100101 Peoples R China Beijing Informat Sci & Technol Univ Minist Educ Key Lab Modern Measurement & Control Technol Beijing 100101 Peoples R China Hebei Agr Univ Sch Informat Sci & Technol Baoding 071001 Peoples R China

The performance of measuring moving vehicle targets with onboard millimeter-wave (MMW) radar can be affected by environmental noise, as well as the dynamic changes in the number of these moving vehicles and the problem of interference of measurement data between adjacent vehicles. In this article, we propose a new possibilistic clustering algorithm for this problem. The radar measurement dataset will contain a lot of information about stationary targets prior to cluster analysis. To obtain a moving target measurement dataset, a method for recognizing stationary and moving target measurements was developed. Then, based on the moving vehicle target measurement dataset, it is initialized with an improved fast density peaks (IFDPs) clustering algorithm, and an enhanced adaptive possibilistic c-means (EAPCM) clustering algorithm is implemented. Because the EAPCM algorithm has the ability to delete redundant clusters but does not increase the clusters, the number of clusters obtained by the IFDP algorithm will be greater than that obtained by the fast density peak (FDP) algorithm. Furthermore, the EAPCM algorithm improved the clustering center point of the adaptive probabilistic c-Means (APCM) algorithm, and replaced it with an adaptive line segment, which makes the algorithm adapt to the vehicle-mounted MMW radar measurement dataset. Two different experiments are carried out in this article, the results show the stability and reliability of the stationary and moving vehicle target recognition method in extracting the measurement values of moving targets, and when compared to other algorithms, the EAPCM algorithm has a higher classification rate (CR) and matching accuracy.

关键词： Adaptive line segment clustering algorithm millimeter-wave (MMW) radar moving target

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clustering overhead and convergence time analysis of the mobility-based multi-hop clustering algorithm for mobile ad hoc networks

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JOURNAL OF COMPUTER AND SYSTEM SCIENCES 2006年第7期72卷 1144-1155页

作者： Er, Inn Inn Seah, Winston K. G. Natl Univ Singapore Sch Comp Dept Comp Sci Singapore 117543 Singapore Inst Infocomm Res Singapore 119613 Singapore

With the emergence of large mobile ad hoc networks, the ability of existing routing protocols to scale well and function satisfactorily comes into question. clustering has been proposed as a means to divide large networks into groups of suitably smaller sizes such that prevailing MANET routing protocols can be applied. However, the benefits of clustering come at a cost. Clusters take time to form and the clustering algorithms also introduce additional control messages that contend with data traffic for the wireless bandwidth. In this paper, we aim to analyse a distributed multi-hop clustering algorithm, Mobility-based D-Hop (MobDHop), based on two key clustering performance metrics and compare it with other popular clustering algorithms used in MANETs. We show that the overhead incurred by multi-hop clustering has a similar asymptotic bound as 1-hop clustering while being able to reap the benefits of multi-hop clusters. Simulation results are presented to verify our analysis. (C) 2006 Elsevier Inc. All rights reserved.

关键词： MobDHop clustering algorithm mobile ad hoc network time complexity message complexity

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Improvement and application of information communication technology in wireless routing protocol based on adaptive K-means clustering algorithm

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WIRELESS NETWORKS 2024年第6期30卷 5997-6009页

作者： Chen, Zili Pan, Yanyan Fujian Chuanzheng Commun Coll Inst Gen Aviat Ind Fuzhou 350007 Peoples R China

With the continuous development of information and communication network technology and data mining technology, an ICT routing algorithm based on improved k-means clustering algorithm and gray wolf optimization is proposed to improve the energy efficiency and extend the life cycle of wireless communication networks. Firstly, we model the atmospheric channel, design the topology of the tolerant network and optimize the routing algorithm to achieve the interoperability between multiple networks and break through the traditional network requirements for time and space constraints. Then we implement an IPv6-based soft router supporting Anycast routing protocol under Linux environment and propose a new Anycast routing implementation scheme. Finally, an ICT routing algorithm (KGRA algorithm) based on improved k-means clustering and gray wolf optimization is designed, which first clusters the sensor nodes in the monitoring area using the improved k-means clustering algorithm. The clusters formed by the improved k-means clustering algorithm are optimized, and then the grey wolf optimization algorithm with improved convergence factor is used to select the cluster heads in the optimized cluster area. Finally, the nodes in the monitoring area will send data to the aggregation nodes using single-hop intra-cluster and multi-hop inter-cluster transmission, thus increasing the survival period of the network and greatly reducing energy consumption while balancing the energy consumption of the network.

关键词： Information communication K-means clustering algorithm Wireless routing To improve the application

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V3COCA: An effective clustering algorithm for complicated objects and its application in breast cancer research and diagnosis

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SIMULATION MODELLING PRACTICE AND THEORY 2009年第2期17卷 454-470页

作者： Wang, Kun Du, Zhihui Chen, Yinong Li, Sanli Tsinghua Univ Dept Comp Sci & Technol Tsinghua Natl Lab Informat Sci & Technol Beijing 100084 Peoples R China Arizona State Univ Dept Comp Sci & Engn Tempe AZ 85287 USA

In breast cancer studies. researchers often use clustering algorithms to investigate similarity/dissimilarity among different cancer cases. The clustering algorithm design becomes a key factor to provide intrinsic disease information. However, the traditional algorithms do not meet the latest multiple requirements simultaneously for breast cancer objects. The Variable parameters, Variable densities, Variable weights, and Complicated Objects clustering algorithm (V3COCA) presented in this paper can handle these problems very well. The V3COCA (1) enables alternative inputs of none or a series of objects for disease research and computer aided diagnosis;(2) proposes an automatic parameter calculation strategy to create Clusters with different densities;(3) enables noises recognition, and generates arbitrary Shaped Clusters: and (4) defines a flexibly weighted distance for measuring the dissimilarity between two complicated medical objects, which emphasizes certain medically concerned issues in the objects. The experimental results with 10,000 patient cases from SEER database show that V3COCA can not only meet the various requirements of complicated Objects clustering, but also be as efficient as the traditional clustering algorithms. (C) 2008 Elsevier B.V. All rights reserved.

关键词： clustering algorithm Breast cancer Complicated objects

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EIGENVALUE APPROACH clustering-algorithm FOR BUILDING EQUIVALENT MODELS OF DISTRIBUTION-SYSTEMS

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IEE PROCEEDINGS-GENERATION TRANSMISSION AND DISTRIBUTION 1995年第3期142卷 282-288页

作者： FOUDA, SS SALAMA, MMA VANNELLI, A CHIKHANI, AY ROYAL MIL COLL CANADA DEPT ELECT & COMP ENGNKINGSTONON K7K 5L0CANADA

A method for representing a large distribution system is developed. The method is based on utilising the clustering technique to build an equivalent distribution system. The behaviour of the original large system may then be studied by analysing the equivalent (simpler) system, with less computational time and with high accuracy. Such behavioural features may include voltage regulation, equipment loading and total system losses. The use of the equivalent system leads to saving in both computer and distribution systems operator(s) time. The use of the clustering techniques significantly reduces the complexity of the problem yet, at the same time, provides very accurate results.

关键词： clustering algorithm DISTRIBUTION SYSTEMS EIGENVALUE APPROACH

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The application of a knowledge-based clustering algorithm as an aid to probe selection and inspection process planning

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 1998年第4期212卷 299-305页

作者： Ajmal, A Zhang, SG S Bank Univ Sch Engn Syst & Design London SE1 0AA England

This paper outlines the development of a clustering algorithm used for inspection planning which allows each inspection feature to be inspected at a designated cell. This is achieved by grouping (a) inspection features into feature families and (b) probe orientations into probe cells. This would result in minimal probe calibration errors and part installation errors for the relative tolerance features. This procedure would reduce the time for probe exchange and reinstallation of parts. An incidence matrix representation has been developed to represent the relationship between inspection features and their relative probe orientations. The incidence matrix which is used for grouping feature families and probe cells are similar in function to the concept of group technology (GT) as used in machine cell formation. The knowledge-based clustering algorithm possesses the flexibility for consideration of multiple constraints for grouping probe cells and feature families. The application of the developed clustering algorithm satisfies the requirement of the inspection feature grouping and provides efficiency and effectiveness in probe selection and inspection process planning for coordinate measuring machines (CMMs).

关键词： clustering algorithm automated inspection planning knowledge-based systems coordinate measuring machines

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Application of big data optimized clustering algorithm in cloud computing environment in traffic accident forecast

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2021年第4期14卷 2511-2523页

作者： Tian, Zhun Zhang, Shengrui Changan Univ Coll Transportat Engn Xian 710064 Shaanxi Peoples R China Xian Univ Architecture & Technol Sch Civil Engn Xian 710055 Shaanxi Peoples R China Changan Univ Key Lab Transport Ind Management Control & Cycle Xian 710064 Shaanxi Peoples R China

As the usage rate of cars is getting higher and higher, the injuries and losses caused by traffic accidents are also getting bigger and bigger. If some traffic accidents can be predicted, then such losses can be greatly solved. Although there are abundant research results on intelligent transportation, there are not many research results on how to predict traffic accidents. For this issue, the main aim of this paper is to propose a continuous non-convex optimization of the K-means algorithm in order to solve the model problem in the traffic prediction process. First, this paper uses clustering algorithm for feature analysis and big data for the establishment of simulation model in cloud environment. Through this paper an equivalent model, using matrix optimization theory to analyze and process K-means problem, and design efficient and theoretically guaranteed algorithms for big data. By simulating the traffic situation in Shanghai city within three years, the outcomes display that the model endorsed in the given paper can predict traffic accidents at a rate of 93.88% and the accuracy rate of traffic accident processing time is 78%, which fully illustrates the effectiveness of the model established in this paper.

关键词： Cloud computing Big data clustering algorithm Traffic accident prediction algorithm optimization

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