检索结果-内蒙古大学图书馆

Customer segmentation using k-means clustering and the adaptive particle swarm optimization algorithm

APPLIED SOFT COMPUTING 2021年第PartB期113卷 107924-107924页

作者： Li, Yue Chu, Xiaoquan Tian, Dong Feng, Jianying Mu, Weisong China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China Minist Agr Key Lab Viticulture & Enol Beijing 100083 Peoples R China

The improvement of enterprise competitiveness depends on the ability to match segmented customers in a competitive market. In this study, we propose a customer segmentation method based on the improved k-means algorithm and the adaptive particle swarm optimization (PSO) algorithm. The current PSO algorithm can easily fall into a local extremum;thus, adaptive learning PSO (ALPSO) is proposed to improve the optimization accuracy. On the basis of the analysis of population-based optimization, the inertia weight, learning factors, and the position update method are redesigned. To prevent the k-means clustering algorithm from depending on initial cluster centres, the ALPSO algorithm is used to optimize the k-means cluster centres (kM-ALPSO). Aimed at the issue of clustering the actual grape-customer consumption mixed dataset, factor analysis is used to extract numerical variables. We then propose a dissimilarity measurement method to cluster the mixed data. We compare ALPSO with several parameter update methods. We also conduct comparative experiments to compare kM-ALPSO on five UCI datasets. Finally, the improved kM-ALPSO (IkM-ALPSO) clustering algorithm is applied in customer segmentation. All results show that the three proposed methods outperform existing models. The experimental results also demonstrate the effectiveness and practicability of IkM-ALPSO for customer segmentation. (C) 2021 Elsevier B.V. All rights reserved.

关键词： k-means clustering algorithm Particle swarm optimization algorithm Adaptive parameter learning Mixed data Customer segmentation

来源：评论

学校读者我要写书评

暂无评论

k-means clustering algorithm for Large-scale Chinese Commodity Information Web Based on Hadoop

K-means Clustering Algorithm for Large-scale Chinese Commodi...

引用

第十四届分布式计算及其应用国际学术研讨会

作者： Geng Yushui Zhang Lishuo School of Information Qilu University of Technology

With the growing popularity of the network, product information filled in the many pages of the Internet, which you want to get the information you need on these pages tend to consider clustering information, and the current explosive growth of data so that the information mass storage condition occurs, clustering to facing the problems such as large calculation complexity and time consuming, then the traditional k-means clustering algorithm does not meet the needs of large data environments today, so this article combined with the advantages of the Hadoop platform and MapReduce programming model is proposed the k-means clustering algorithm for large-scale chinese commodity information Web based on Hadoop. Map function calculates the distance from the cluster center for each sample and mark to their category, Reduce function intermediate results are summarized and calculated new clustering center for the next round of iteration. Experimental results show that this method can better improve the clustering processing speed.

关键词： k-means clustering algorithm Hadoop platform MapReduce Cloud computing Big Data

来源：评论

学校读者我要写书评

暂无评论

Discrete phase CV-QkD over a hybrid free space - fibre channel with k-means clustering

Discrete phase CV-QKD over a hybrid free space - fibre chann...

引用

Conference on Quantum Technologies

作者： Vilashini, Saravana M. Prabhakara, Anil Indian Inst Technol Madras Chennai 600036 India

ISBN: (纸本)9781510673052;9781510673045

Quadrature phase shift keying continuous variable quantum key distribution (QPSk CV-QkD) can be used to encode bits of information on weak coherent states, enhanced with quantum scissors. In our experiment, we use discretely modulated QPSk CV-QkD across a 150 m long free-space optical (FSO) link over the two buildings with a four symbol constellation. Other CV-QkD deployments in the literature have demonstrated a Frame Error Rate of 20% with a 100 MHz repetition rate and a loss of 6 dB in the channel. Our setup has a QBER of 25% for a 35 dB channel loss. Our system has been tested for a link loss of upto 50 dB. The QPSk symbols generated at 100 MHz from a Zynq Ultrascale+ Field Programmable Gated Array and fed to an I/Q transmitter, which in turn modulated a 1550 nm laser. The FSO transmitter, connected to our lab 500 m away, consists of a collimator and a beam expander. The transmitted states were captured on the other side by a FSO receiver unit consisting of a collimator. The received signal was transported back to the lab via a fiber of length 1 km, which is then passed through a 90 degree hybrid coherent receiver, with a 1 GHz bandwidth. The captured quadratures are post processed with a k-means cluster algorithm allowing us to recover the bit values. Our experiments focus on understanding the change in BER in the presence of varying levels of attenuation over the hybrid FSO+fibre channel.

关键词： Quadrature phase shift keying Free Space Optics Continuous Variable Quantum key Distribution k-means clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

Automatically Extracting Bridge Frequencies Using SSA and k-means clustering from Vehicle-Scanned Accelerations

引用

INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS 2022年第8期22卷 2250079-2250079页

作者： Yang, Y. B. He, Yi Xu, Hao Chongqing Univ Res Ctr Engn Vibrat & Disaster Prevent Chongqing 400045 Peoples R China Chongqing Univ Sch Civil Engn Chongqing 400045 Peoples R China Chongqing Univ Sci & Technol Sch Civil Engn & Architecture Chongqing Peoples R China

This paper removes the subjective judgment by proposing a technique for automatically identifying the bridge frequencies using singularity spectrum analysis and k-means clustering. First, the vehicle-bridge contact-point acceleration newly derived from is used to avoid the adverse effect brought by vehicle's frequency. Next, the contact signal is processed by singular spectrum analysis to remove the noises. Then, the first several principal components are processed by the FFT to extract peak frequencies. Finally, the k-means clustering algorithm is adopted to group the peak frequencies into clusters, with their centroids denoting the bridge frequencies. They are verified against those extrapolated from the 1st bridge frequency. Moreover, the current technique was assessed by studies on factors including pavement roughness, vehicle velocity, ongoing traffic, and vehicle stiffness, along with a field test on a bridge to demonstrate its capability. One feature of the current method is that the errors for higher frequencies predicted are not higher than lower frequencies.

关键词： Contact-point acceleration pavement roughness singular spectrum analysis k-means clustering algorithm bridge frequency

来源：评论

学校读者我要写书评

暂无评论

Multipath/NLOS Detection Based on k-means clustering for GNSS/INS Tightly Coupled System in Urban Areas

引用

MICROMACHINES 2022年第7期13卷 1128-1128页

作者： Wang, Hao Pan, Shuguo Gao, Wang Xia, Yan Ma, Chun Southeast Univ Sch Instrument Sci & Engn Nanjing 210096 Peoples R China Southeast Univ Key Lab Microinertial Instrument & Adv Nav Techno Nanjing 210096 Peoples R China Nanjing Tech Univ Sch Geomat Sci & Technol Nanjing 211800 Peoples R China

Due to the massive multipath effects and non-line-of-sight (NLOS) signal receptions, the accuracy and reliability of GNSS positioning solution can be severely degraded in a highly urbanized area, which has a negative impact on the performance of GNSS/INS integrated navigation. Therefore, this paper proposes a multipath/NLOS detection method based on the k-means clustering algorithm for vehicle GNSS/INS integrated positioning. It comprehensively considers different feature parameters derived from GNSS raw observations, such as the satellite-elevation angle, carrier-to-noise ratio, pseudorange residual, and pseudorange rate consistency to effectively classify GNSS signals. In view of the influence of different GNSS signals on positioning results, the k-means clustering algorithm is exploited to divide the observation data into two main categories: direct signals and indirect signals (including multipath and NLOS signals). Then, the multipath/NLOS signal is separated from the observation data. Finally, this paper uses the measured vehicle GNSS/INS observation data, including offline dataset and online dataset, to verify the accuracy of signal classification based on double-differenced pseudorange positioning. A series of experiments conducted in typical urban scenarios demonstrate that the proposed method could ameliorate the positioning accuracy significantly compared with the conventional GNSS/INS integrated navigation. After excluding GNSS outliers, the positioning accuracy of the offline dataset is improved by 16% and 85% in the horizontal and vertical directions, respectively, and the positioning accuracy of the online dataset is improved by 21% and 41% in the two directions. This method does not rely on external geographic information data and other sensors, which has better practicability and environmental adaptability.

关键词： GNSS INS tightly coupled system multipath NLOS detection k-means clustering algorithm urban areas

来源：评论

学校读者我要写书评

暂无评论

Research of outliers in time series of stock prices based on improved k-means clustering algorithm

Research of outliers in time series of stock prices based on...

引用

International Conference of Information Science and Management Engineering (ISME)

作者： Zhu, Xi Che, Wen-Gang Kunming Univ Sci & Technol Sch Informat Engn & Automat Kunming Peoples R China

ISBN: (纸本)9781845648299;9781845648282

The outlier in financial time series usually can affect the trend of the series, at the same time, it often indicates social public information to the influence of the stock market. So how to detect the outlier in financial time series effectively is of great significance. This paper is based on the clustering of the outlier detection by the idea, and put forward an improved k-means clustering algorithm. The main feature of the improved algorithm is to use the idea of the clustering of the outlier detection, this algorithm used the inherent disadvantage of the k-means algorithm which is easy to fall into the local extrema. So we changed clusters k and made clusters k=10k. Then we let k decrease to k in a certain step size, In this process, we had repeated the cycle to identify the outlier, so that we could ensure this algorithm will not always fall into the local extrema. In this paper, on the closing price of the Shanghai Composite Index and China International Trade Stock and macroeconomic information empirical analysis, we prove that the method is feasible and effective.

关键词： Time series outlier k-means clustering algorithm local extrema

来源：评论

学校读者我要写书评

暂无评论

An improved method for k-means clustering based on internal validity indexes and inter-cluster variance

引用

INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2022年第3期25卷 253-261页

作者： Zhu, Guangli Li, Xiaoqing Zhang, Shunxiang Xu, Xin Zhang, Biao Anhui Univ Sci & Technol Sch Comp Sci & Engn Huainan Peoples R China

It is difficult to get the best cluster number of k-means clustering algorithm by using the traditional internal validity indexes. Therefore, a good clustering result cannot be obtained usually. To solve this problem, this paper proposes an improved method for k-means clustering based on internal validity indexes and inter-cluster variance. Firstly, some integers distributed in the interval [2, root n] are selected as initial cluster numbers. Further, each initial cluster number is selected to carry out k-means clustering and obtain a clustering result. Secondly, two initial cluster numbers k(D) and k(C) are extracted respectively under two optimal validity index values (i.e., DB and CH). The k(D) and k(C) are extracted by comparing and analysing the validity index values. Finally, the validity index ICS-VAR is proposed to select the best cluster number k(B), while k(D) is not equal to k(C). Experimental results show that the improved method can obtain a better cluster number under a certain condition.

关键词： k-means clustering algorithm internal validity indexes inter-cluster variance initial cluster number

来源：评论

学校读者我要写书评

暂无评论

The Classification System of Literary Works Based on k-means clustering

引用

JOURNAL OF INTERCONNECTION NETWORkS 2022年第SUPP01期22卷 2141001-2141001页

作者： Wei, Sanqiang Hou, Hongxia Sun, Hua Li, Wei Song, Wenxia Xingtai Univ Xingtai 054001 Hebei Peoples R China

The plots in certain literary works are very complicated and hinder readers from understanding them. Therefore tools should be proposed to support readers;comprehension of complex literary works supports their understanding by providing the most important information to readers. A human reader must capture multiple levels of abstraction and meaning to formulate an understanding of a document. Hence, in this paper, an Improved k-means clustering algorithm (IkCA) has been proposed for literary word classification. For text data, the words that can express exact semantic in a class are generally better features. This paper uses the proposed technique to capture numerous cluster centroids for every class and then select the high-frequency words in centroids the text features for classification. Furthermore, neural networks have been used to classify text documents and k-mean to cluster text documents. To develop the model based on unsupervised and supervised techniques to meet and identify the similarity between documents. The numerical results show that the suggested model will enhance to increases quality comparison of the existing algorithm and k-means algorithm, accuracy comparison of ALA and IkCA (95.2%), time is taken for clustering is less than 2 hours, success rate (97.4%) and performance ratio (98.1%).

关键词： Literary works k-means clustering algorithm text classification

来源：评论

学校读者我要写书评

暂无评论

Multifactor diagnostic model of converter energy consumption based on k-means algorithm and its application

引用

JOURNAL OF IRON AND STEEL RESEARCH INTERNATIONAL 2025年 1-11页

作者： Dai, Fei-xiang Chen, Guang Bao, Xiang-jun Liu, Gong-guo Zhang, Lu Yang, Xiao-jing Anhui Univ Technol Sch Energy & Environm Maanshan 243002 Anhui Peoples R China Pangang Grp Res Inst Co Ltd Panzhihua 617000 Sichuan Peoples R China

To address the challenge of identifying the primary causes of energy consumption fluctuations and accurately assessing the influence of various factors in the converter unit of an iron and steel plant, the focus is placed on the critical components of material and heat balance. Through a thorough analysis of the interactions between various components and energy consumptions, six pivotal factors have been identified-raw material composition, steel type, steel temperature, slag temperature, recycling practices, and operational parameters. Utilizing a framework based on an equivalent energy consumption model, an integrated intelligent diagnostic model has been developed that encapsulates these factors, providing a comprehensive assessment tool for converter energy consumption. Employing the k-means clustering algorithm, historical operational data from the converter have been meticulously analyzed to determine baseline values for essential variables such as energy consumption and recovery rates. Building upon this data-driven foundation, an innovative online system for the intelligent diagnosis of converter energy consumption has been crafted and implemented, enhancing the precision and efficiency of energy management. Upon implementation with energy consumption data at a steel plant in 2023, the diagnostic analysis performed by the system exposed significant variations in energy usage across different converter units. The analysis revealed that the most significant factor influencing the variation in energy consumption for both furnaces was the steel grade, with contributions of -0.550 and 0.379.

关键词： Equivalent energy consumption model Intelligent diagnostic model k-means clustering algorithm Online system Energy management

来源：评论

学校读者我要写书评

暂无评论

Wind-Photovoltaic-Hydropower Joint Output Model Study Based on Probability Distribution and Correlation Analysis

引用

ENERGIES 2025年第10期18卷 2511-2511页

作者： Wu, Ligui Wang, Benhong Zhang, Peng ke, Yiming Zhang, Fangqing Guo, Jiang China Yangtze Power Co Ltd Yichang 443000 Peoples R China Wuhan Univ Sch Power & Mech Engn Wuhan 430072 Peoples R China

To maximize the consumption of renewable energy and improve the reliability of power systems, the development of wind-photovoltaic-hydropower complementary power systems is promising, utilizing the dynamic regulation characteristics of hydropower. Firstly, the complementarity of wind-photovoltaic-hydropower is analyzed, where output characteristics of wind, photovoltaics, and hydropower are introduced, and output models related to wind speed, light intensity, and water level are established. Furthermore, the probability distribution of wind and photovoltaic output is calculated with the maximum likelihood method, and a correlation analysis between wind and photovoltaic output is conducted, where a wind-photovoltaic joint output model is established. Lastly, the k-means clustering algorithm is adopted to process typical scenarios of wind-photovoltaic joint output, and a case study is conducted to validate the wind-photovoltaic-hydropower joint output model. In configuring wind and photovoltaic output reasonably, the amount of abandoned wind and abandoned light is decreased significantly, while the reliability of the power system is improved.

关键词： renewable energy complementary power system probability distribution correlation analysis k-means clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：