检索结果-内蒙古大学图书馆

A proposed hybrid clustering algorithm using k-means and BIRCH for cluster based cab recommender system (CBCRS)

International Journal of Information Technology (Singapore) 2023年第1期15卷 219-227页

作者： Mann, Supreet kaur Chawla, Sonal Panjab University Panjab Chandigarh India

An efficient Cluster Based Cab Recommender System (CBCRS) assists the cab drivers with the recommendations about passenger pickup location available at the shortest distance from him. To recommend drivers about the passenger pickup location, one need to group the Global Positioning System (GPS) coordinates of several pickup points of the same geographic region. The GPS coordinates of cab pick-up points are unsupervised data. clustering of unsupervised cab dataset is troublesome since cab dataset is a large database and clustering techniques when applied on such large datasets do not generate good clusters for GPS datapoints. Therefore, this research paper proposes an improved hybrid clustering algorithm which combines the features of Partition-based clustering and Hierarchical Based clustering techniques. Thus, the objectives of the research paper are four folds: firstly, the research paper identifies various clustering techniques to cluster GPS Coordinates. Secondly, to design and develop an improved hybrid clustering algorithm for CBCRS. Thirdly, the research paper compares the clusters formed by the proposed algorithm with standard k-means and Balanced Iterative Reducing and clustering using Hierarchies (BIRCH) using three datasets over Silhouette Coefficient and Calinski-Harabasz Score. Finally, the paper concludes and analyses the results of the proposed algorithm. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： BIRCH clustering algorithm Calinski-Harabasz score Hybrid clustering algorithm k-means clustering algorithm Recommender system Silhouette score Unsupervised machine learning

来源：评论

学校读者我要写书评

暂无评论

A new data-adaptive network design methodology based on the k-means clustering and modified ISODATA algorithm for regional gravity field modeling via spherical radial basis functions

引用

JOURNAL OF GEODESY 2022年第11期96卷 91-91页

作者： Ulug, Rasit karslioglu, Mahmut Onur Middle East Tech Univ Dept Geodet & Geog Informat Technol TR-068000 Ankara Turkey Middle East Tech Univ Dept Civil Engn Geomat Engn Div Ankara Turkey

In this study, a new data-adaptive network design methodology called k-SRBF is presented for the spherical radial basis functions (SRBFs) in regional gravity field modeling. In this methodology, the cluster centers (centroids) obtained by the k-means clustering algorithm are post-processed to construct a network of SRBFs by replacing the centroids with the SRBFs. The post-processing procedure is inspired by the heuristic method, Iterative Self-Organizing Data Analysis Technique (ISODATA), which splits clusters within the user-defined criteria to avoid over- and under-parameterization. These criteria are the minimum spherical distance between the centroids and the minimum number of samples for each cluster. The bandwidth (depth) of each SRBF is determined using the generalized cross-validation (GCV) technique in which only the observations within the radius of impact area (RIA) are used. The numerical tests are carried out with real and simulated data sets to investigate the effect of the user-defined criteria on the network design. Different bandwidth limits are also examined, and the appropriate lower and upper bandwidth limits are chosen based on the empirical signal covariance function and user-defined criteria. Also, additional tests are performed to verify the performance of the proposed methodology in combining different types of observations, such as terrestrial and airborne data available in Colorado. The results reveal that k-SRBF is an effective methodology to establish a data-adaptive network for SRBFs. Moreover, the proposed methodology improves the condition number of normal equation matrix so that the least-squares procedure can be applied without regularization considering the user-defined criteria and bandwidth limits.

关键词： Regional gravity field modeling Spherical radial basis functions Data-adaptive network design Poisson kernel k-means clustering algorithm ISODATA

来源：评论

学校读者我要写书评

暂无评论

Content-based image retrieval using PSO and k-means clustering algorithm

引用

ARABIAN JOURNAL OF GEOSCIENCES 2015年第8期8卷 6211-6224页

作者： Younus, Zeyad Safaa Mohamad, Dzulkifli Saba, Tanzila Alkawaz, Mohammed Hazim Rehman, Amjad Al-Rodhaan, Mznah Al-Dhelaan, Abdullah Univ Teknol Malaysia Fac Comp Johor Baharu Malaysia Univ Mosul Fac Comp Sci & Math Mosul Iraq Prince Sultan Univ Coll Comp & Informat Sci Riyadh Saudi Arabia Salman Bin Abdulaziz Univ MIS Dept CBA Alkharj Saudi Arabia King Saud Univ Coll Comp & Informat Sci Dept Comp Sci Riyadh Saudi Arabia

In various application domains such as website, education, crime prevention, commerce, and biomedicine, the volume of digital data is increasing rapidly. The trouble appears when retrieving the data from the storage media because some of the existing methods compare the query image with all images in the database;as a result, the search space and computational complexity will increase, respectively. The content-based image retrieval (CBIR) methods aim to retrieve images accurately from large image databases similar to the query image based on the similarity between image features. In this study, a new hybrid method has been proposed for image clustering based on combining the particle swarm optimization (PSO) with k-means clustering algorithms. It is presented as a proposed CBIR method that uses the color and texture images as visual features to represent the images. The proposed method is based on four feature extractions for measuring the similarity, which are color histogram, color moment, co-occurrence matrices, and wavelet moment. The experimental results have indicated that the proposed system has a superior performance compared to the other system in terms of accuracy.

关键词： Content-based image retrieval CBIR k-means clustering algorithm Feature extraction Co-occurrence matrix Similarity index

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 14

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

引用

International Symposium Distributed Computing and Applications Business Engineering and Science

作者： Geng Yushui Zhang Lishuo Qilu Univ Technol Sch Informat Jinan 250353 Peoples R China

ISBN: (纸本)9781467365932

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

Identification of column edges of DNA fragments by using k-means clustering and mean algorithm on lane histograms of DNA agarose gel electrophoresis images 7

Identification of column edges of DNA fragments by using K-m...

引用

7th International Conference on Digital Image Processing (ICDIP)

作者： Turan, Muhammed kamil Sehirli, Eftal Elen, Abdullah karas, Ismail Rakip Karabuk Univ Karabuk Turkey

ISBN: (纸本)9781628418293

Gel electrophoresis (GE) is one of the most used method to separate DNA, RNA, protein molecules according to size, weight and quantity parameters in many areas such as genetics, molecular biology, biochemistry, microbiology. The main way to separate each molecule is to find borders of each molecule fragment. This paper presents a software application that show columns edges of DNA fragments in 3 steps. In the first step the application obtains lane histograms of agarose gel electrophoresis images by doing projection based on x-axis. In the second step, it utilizes k-means clustering algorithm to classify point values of lane histogram such as left side values, right side values and undesired values. In the third step, column edges of DNA fragments is shown by using mean algorithm and mathematical processes to separate DNA fragments from the background in a fully automated way. In addition to this, the application presents locations of DNA fragments and how many DNA fragments exist on images captured by a scientific camera.

关键词： Lane histogram k-means clustering algorithm mean algorithm DNA fragments

来源：评论

学校读者我要写书评

暂无评论

A Hybrid k-means Method based on Modified Rat Swarm Optimization algorithm for Data clustering

A Hybrid K-means Method based on Modified Rat Swarm Optimiza...

引用

第43届中国控制会议

作者： Taishan Lou Guanguang Guan Zhepeng Yue Yu Wang Shihao Tong School of Electrical and Information Engineering Zhengzhou University of Light Industry

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The original k-means clustering algorithm is prone to local optima and sensitive to the initial clustering center,which have a great impact on accuracy and stability of clustering results in practical *** overcome this limitation,an innovative k-means clustering method based on modified rat swarm optimization(RSO) algorithm is proposed.A nonlinear convergence factor is introduced into the RSO to adjust convergence speed of different data sets and improve the global search ***,a reverse initial population strategy is adopted to increase population diversity,thus improving the robustness of the algorithm to the initial *** modified RSO algorithm is used to find the initial optimal cluster centroid,and then k-means algorithm is used to refine the optimized initial cluster centroid to improve the clustering *** experimental results show that compared with the original k-means clustering algorithm,the improved algorithm has achieved significant improvement in each index of iris,wine and glass datasets,which proves the effectiveness and superiority of the *** paper presents a new hybrid clustering algorithm which combines the improved swarm intelligent optimization algorithm with k-means clustering *** method effectively solves the problem that k-means clustering algorithm is sensitive to the initial cluster center.

关键词： k-means clustering algorithm Rat Swarm Optimization global search ability

来源：评论

学校读者我要写书评

暂无评论

k-means clustering Based on Improved Quantum Particle Swarm Optimization algorithm 13

K-means Clustering Based on Improved Quantum Particle Swarm ...

引用

13th International Conference on Advanced Computational Intelligence (ICACI)

作者： Bai, Lili Song, Zerui Bao, Haijie Jiang, Jingqing Inner Mongolia Univ Nationalities Coll Comp Sci & Technol Tongliao Peoples R China Inner Mongolia Univ Nationalities Coll Math & Phys Tongliao Peoples R China

ISBN: (纸本)9781665412544

In clustering, in order to find a better data clustering center, make the algorithm convergence faster and clustering results more accurate, a k-means clustering algorithm based on improved quantum particle swarm optimization algorithm is proposed. In this algorithm, the cluster center is simulated as a particle. Cloning and mutation operations are used to increase the diversity and improve the global search ability of QPSO. A suitable and stable cluster center is obtained. Finally, an effective clustering result is obtained. The algorithm is tested with UCI data set. The results show that the improved algorithm not only ensures the global convergence of the algorithm, but also obtains more accurate clustering results.

关键词： k-means clustering algorithm Quantum Particle Swarm Optimization algorithm QPSO-k-means algorithm Cloning Mutation

来源：评论

学校读者我要写书评

暂无评论

Flexible resource allocation optimization model considering global k-means load clustering and renewable-energy consumption

引用

Clean Energy 2024年第5期8卷 129-143页

作者： Jie Jiao Puyu He Yuhong Zhang Jiyuan Zhang Zhuhan Long Hanjing Liu State Grid Sichuan Electric Power Company Economic Research Institute ChengduChina

Vigorously developing flexible resources in power systems will be the key to building a new power system and realizing energy *** investment construction cost and operation cost of various flexible resources are different,and the adjustment ability is different in different ***,the optimization of complementary allocation of various resources needs to take into account the economy and adjustment ability of different *** this paper,the global k-means load clustering model is pro-posed and the 365-day net load is reduced to eight typical daily net loads by ***,a two-level optimization model of flexible resource complementary allocation considering wind power and photovoltaic consumption is *** flexible resources involved include the flexible transformation of thermal power,hydropower,pumped storage,energy storage,and demand *** upper-layer model optimizes the capacity allocation of various flexible resources with the minimum investment and construction cost as the goal and the lower layer optimizes the operating output of various units with the minimum operating cost as the *** results of the example analysis show that the flexible capacity of thermal power units has nothing to do with the abandonment rate of renewable *** the abandonment rate of renewable energy decreases,the optimal capacity of pumped storage,electrochemical energy storage,and hydropower units *** the power-abandonment rate of renewable energy is 5%,the optimal allocation capacity of thermal power flexibility transformation,pumped storage,electrochemical energy storage,hydropower unit,and adjustable load in Province A is 5313,17090,5830,72113,and 4250 MW,*** the condition that the renewable-energy abandonment rate is 0,5%,and 10%respectively,the configured capacity of pumped storage is 20000,17090,and 14847 MW,respectively.

关键词： wind power photovoltaic power k-means clustering algorithm flexible resources complementary configuration operation optimization

来源：评论

学校读者我要写书评

暂无评论

A Lightweight Mutual Privacy Preserving k-means clustering in Industrial IoT

引用

IEEE TRANSACTIONS ON NETWORk SCIENCE AND ENGINEERING 2024年第2期11卷 2138-2152页

作者： Hu, Chunqiang Liu, Jianshuo Xia, Hui Deng, Shaojiang Yu, Jiguo Chongqing Univ Sch Big Data & Software Engn Chongqing 400030 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400030 Peoples R China Ocean Univ China Coll Comp Sci & Technol Qingdao 266100 Peoples R China Qilu Univ Technol Big Data Inst Jinan 250316 Peoples R China

In many industrial Internet of Things application schemes, participants, such as IoT devices, often share information with each others for analysis and categorization purpose. Through the analysis and statistical processing of the information, some necessary information is exchanged, e.g., the attributes in manufacturing IoT devices' gathered data. In this context, many devices are necessarily trusted when sharing information, which raises concerns about data privacy leakage. As the number of IoT devices in the network increases, the scale of pairwise keys increases rapidly. Furthermore, the limitation of device's computing ability makes it hard to perform centralized computing. To address these issues, we propose a light weight k-means clustering scheme that performs clustering with high accuracy, maintaining at a low key management cost, and securing the private attributes of each participant or the intermediate variables. In our proposed scheme, we use aprivate optimal initial cluster center generation algorithm based on attribute weights, in order to achieve the better clustering quality. Secondly, we securely find the nearest clustering center for each participant. We dynamically split and merge clustering centers respectively, in order to meet the optimal clustering result. Last but not least, we calculate the clustering centers without revealing any private attributes about the participants' information during the process. The analysis indicates that our proposed scheme canresist collusion attacks and ensures that the cost of overall pair wisekey management and computation cost performed in data center are relatively low.

关键词： k-means clustering algorithm privacy preservation lightweight clustering algorithm homomorphic encryption

来源：评论

学校读者我要写书评

暂无评论

Construction of aesthetic education evaluation model based on k-means clustering analysis in the context of artificial intelligence

引用

JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年第1期25卷 728-743页

作者： Li, Mo Guo, Shasha Chen, Jie Hebei Univ Engn Student Affairs Off Handan Peoples R China Hebei Univ Engn Sch Earth Sci & Engn 19Taiji RdEcon Devel Zone Handan 056038 Hebei Province Peoples R China

Enhancing aesthetic education can boost individuals' skills in recognizing, understanding, appreciating, and creating beauty. Nevertheless, traditional methods for evaluating aesthetic education are often subjective and have limited capacity to handle complex features effectively. This article was based on artificial intelligence to develop an aesthetic education evaluation model, and used an improved k-means clustering algorithm and a pre-trained ResNet-101 model for feature extraction. This article used WikiArt as an experimental data set, which contains 77,229 artworks from 15 art styles. This study used silhouette coefficient, Davies-Bouldin index, Calinski-Harabasz index, and Dunn index to evaluate the model clustering effect. Experimental results indicated that with a clustering \(k\) value of 10, the enhanced algorithm significantly advanced clustering quality, computational efficiency, and memory usage. This model has enhanced the objectivity and efficiency of aesthetic education evaluation and introduced new approaches for classifying and assessing artistic works.

关键词： k-means clustering algorithm aesthetic education evaluation artificial intelligence feature extraction ResNet-101 model

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：