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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

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Customer Segmentation Using k-means clustering and the Hybrid Particle Swarm Optimization algorithm

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COMPUTER JOURNAL 2023年第4期66卷 941-962页

作者： Li, Yue Qi, Jianfang Chu, Xiaoquan 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

In a competitive market, it is of great significance to divide customer groups to develop customer-centered personalized products. In this paper, we propose a customer segmentation method based on the k-means algorithm and the improved particle swarm optimization (PSO) algorithm. As the PSO algorithm easily falls into local extremum, the improved hybrid particle swarm optimization (IHPSO) algorithm is proposed to improve optimization accuracy. The full factorial design is used to determine the optimal parameter combination;the roulette operator is used to select excellent particles;then, the selected particles are crossed according to their adaptive crossover probabilities;when the population falls into a local optimum, the particles are mutated according to their adaptive mutation probabilities. Aimed at the k-means' sensitivity to selecting the initial cluster centers, IHPSO is used to optimize the cluster centers (IHPSO-kM). We compare IHPSO with the PSO, LDWPSO, GA, GA-PSO and ALPSO algorithms on nine benchmark functions. We also conduct comparative experiments to compare IHPSO-kM with several conventional and state-of-the-art approaches on five UCI datasets. All results show that the two proposed methods outperform existing models. Finally, IHPSO-kM is applied in customer segmentation. The experimental results also prove the rationality and applicability of IHPSO-kM for customer segmentation.

关键词： k-means clustering algorithm particle swarm optimization algorithm hybrid mechanism cluster analysis customer segmentation

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A k-means clustering-Based Multiple Importance Sampling algorithm for Integral Global Optimization

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Journal of the Operations Research Society of China 2023年第1期11卷 157-175页

作者： Chen Wang Dong-Hua Wu Department of Mathematics Shanghai UniversityShanghai200444China

In this paper, we propose a k-means clustering-based integral level-value estimation algorithm to solve a kind of box-constrained global optimization problem. For this purpose, we introduce the generalized variance function associated with the level-value of the objective function to be minimized. The variance function has a good property when Newton’s method is used to solve a variance equation resulting by setting the variance function to zero. We prove that the largest root of the variance equation is equal to the global minimum value of the corresponding optimization problem. Based on the k-means clustering algorithm, the multiple importance sampling technique is proposed in the implementable algorithm. The main idea of the cross-entropy method is used to update the parameters of sampling density function. The asymptotic convergence of the algorithm is proved, and the validity of the algorithm is verified by numerical experiments.

关键词： Global optimization Generalized variance function Multiple importance sampling k-means clustering algorithm

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A Hybrid k-means Method based on Modified Rat Swarm Optimization algorithm for Data clustering 43

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

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43rd Chinese Control Conference (CCC)

作者： Lou, Taishan Guan, Guanguang Yu, Zhepeng Wang, Yu Tong, Shihao Zhengzhou Univ Light Ind Sch Elect & Informat Engn Zhengzhou 450002 Henan Peoples R China

ISBN: (纸本)9798350366907;9789887581581

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 applications. To 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 ability. Then, a reverse initial population strategy is adopted to increase population diversity, thus improving the robustness of the algorithm to the initial conditions. The 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 accuracy. The 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 algorithm. This paper presents a new hybrid clustering algorithm which combines the improved swarm intelligent optimization algorithm with k-means clustering algorithm. This 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

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

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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

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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...

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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

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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...

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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

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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...

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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

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

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

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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

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k-means clustering algorithm for multimedia applications with flexible HW/SW co-design

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JOURNAL OF SYSTEMS ARCHITECTURE 2013年第3期59卷 155-164页

作者： An, Fengwei Mattausch, Hans Juergen Hiroshima Univ Res Inst Nanodevice & Bio Syst Higashihiroshima 724 Japan

In this paper, we report a hardware/software (MW/SW) co-designed k-means clustering algorithm with high flexibility and high performance for machine learning, pattern recognition and multimedia applications. The contributions of this work can be attributed to two aspects. The first is the hardware architecture for nearest neighbor searching, which is used to overcome the main computational cost of a k-means clustering algorithm. The second aspect is the high flexibility for different applications which comes from not only the software but also the hardware. High flexibility with respect to the number of training data samples, the dimensionality of each sample vector, the number of clusters, and the target application, is one of the major shortcomings of dedicated hardware implementations for the k-means algorithm. In particular, the HW/SW k-means algorithm is extendable to embedded systems and mobile devices. We benchmark our multi-purpose k-means system against the application of handwritten digit recognition, face recognition and image segmentation to demonstrate its excellent performance, high flexibility, fast clustering speed, short recognition time, good recognition rate and versatile functionality. (c) 2012 Elsevier B.V. All rights reserved.

关键词： Hardware/software co-design k-means clustering algorithm Nearest neighbor searching Handwritten digit recognition Face recognition Image segmentation

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