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Multi-Objective Optimization of Multi-Product Parallel Disassembly Line Balancing Problem Considering Multi-Skilled Workers Using a Discrete Chemical Reaction Optimization Algorithm
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作者 Xiwang Guo Liangbo Zhou +4 位作者 Zhiwei Zhang Liang Qi Jiacun Wang Shujin Qin Jinrui Cao 《Computers, Materials & Continua》 SCIE EI 2024年第9期4475-4496,共22页
This work investigates a multi-product parallel disassembly line balancing problem considering multi-skilled workers.A mathematical model for the parallel disassembly line is established to achieve maximized disassemb... This work investigates a multi-product parallel disassembly line balancing problem considering multi-skilled workers.A mathematical model for the parallel disassembly line is established to achieve maximized disassembly profit and minimized workstation cycle time.Based on a product’s AND/OR graph,matrices for task-skill,worker-skill,precedence relationships,and disassembly correlations are developed.A multi-objective discrete chemical reaction optimization algorithm is designed.To enhance solution diversity,improvements are made to four reactions:decomposition,synthesis,intermolecular ineffective collision,and wall invalid collision reaction,completing the evolution of molecular individuals.The established model and improved algorithm are applied to ball pen,flashlight,washing machine,and radio combinations,respectively.Introducing a Collaborative Resource Allocation(CRA)strategy based on a Decomposition-Based Multi-Objective Evolutionary Algorithm,the experimental results are compared with four classical algorithms:MOEA/D,MOEAD-CRA,Non-dominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ),and Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ).This validates the feasibility and superiority of the proposed algorithm in parallel disassembly production lines. 展开更多
关键词 parallel disassembly line balancing problem MULTI-PRODUCT multiskilled workers discrete chemical reaction optimization algorithm
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Accelerating Large-Scale Sorting through Parallel Algorithms
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作者 Yahya Alhabboub Fares Almutairi +3 位作者 Mohammed Safhi Yazan Alqahtani Adam Almeedani Yasir Alguwaifli 《Journal of Computer and Communications》 2024年第1期131-138,共8页
This study explores the application of parallel algorithms to enhance large-scale sorting, focusing on the QuickSort method. Implemented in both sequential and parallel forms, the paper provides a detailed comparison ... This study explores the application of parallel algorithms to enhance large-scale sorting, focusing on the QuickSort method. Implemented in both sequential and parallel forms, the paper provides a detailed comparison of their performance. This study investigates the efficacy of both techniques through the lens of array generation and pivot selection to manage datasets of varying sizes. This study meticulously documents the performance metrics, recording 16,499.2 milliseconds for the serial implementation and 16,339 milliseconds for the parallel implementation when sorting an array by using C++ chrono library. These results suggest that while the performance gains of the parallel approach over its serial counterpart are not immediately pronounced for smaller datasets, the benefits are expected to be more substantial as the dataset size increases. 展开更多
关键词 Sorting algorithm Quick Sort QuickSort parallel parallel algorithms
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Plant Leaf Diseases Classification Using Improved K-Means Clustering and SVM Algorithm for Segmentation
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作者 Mona Jamjoom Ahmed Elhadad +1 位作者 Hussein Abulkasim Safia Abbas 《Computers, Materials & Continua》 SCIE EI 2023年第7期367-382,共16页
Several pests feed on leaves,stems,bases,and the entire plant,causing plant illnesses.As a result,it is vital to identify and eliminate the disease before causing any damage to plants.Manually detecting plant disease ... Several pests feed on leaves,stems,bases,and the entire plant,causing plant illnesses.As a result,it is vital to identify and eliminate the disease before causing any damage to plants.Manually detecting plant disease and treating it is pretty challenging in this period.Image processing is employed to detect plant disease since it requires much effort and an extended processing period.The main goal of this study is to discover the disease that affects the plants by creating an image processing system that can recognize and classify four different forms of plant diseases,including Phytophthora infestans,Fusarium graminearum,Puccinia graminis,tomato yellow leaf curl.Therefore,this work uses the Support vector machine(SVM)classifier to detect and classify the plant disease using various steps like image acquisition,Pre-processing,Segmentation,feature extraction,and classification.The gray level co-occurrence matrix(GLCM)and the local binary pattern features(LBP)are used to identify the disease-affected portion of the plant leaf.According to experimental data,the proposed technology can correctly detect and diagnose plant sickness with a 97.2 percent accuracy. 展开更多
关键词 SVM machine learning GLCM algorithm k-means clustering LBP
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Genetic Algorithm Combined with the K-Means Algorithm:A Hybrid Technique for Unsupervised Feature Selection
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作者 Hachemi Bennaceur Meznah Almutairy Norah Alhussain 《Intelligent Automation & Soft Computing》 SCIE 2023年第9期2687-2706,共20页
The dimensionality of data is increasing very rapidly,which creates challenges for most of the current mining and learning algorithms,such as large memory requirements and high computational costs.The literature inclu... The dimensionality of data is increasing very rapidly,which creates challenges for most of the current mining and learning algorithms,such as large memory requirements and high computational costs.The literature includes much research on feature selection for supervised learning.However,feature selection for unsupervised learning has only recently been studied.Finding the subset of features in unsupervised learning that enhances the performance is challenging since the clusters are indeterminate.This work proposes a hybrid technique for unsupervised feature selection called GAk-MEANS,which combines the genetic algorithm(GA)approach with the classical k-Means algorithm.In the proposed algorithm,a new fitness func-tion is designed in addition to new smart crossover and mutation operators.The effectiveness of this algorithm is demonstrated on various datasets.Fur-thermore,the performance of GAk-MEANS has been compared with other genetic algorithms,such as the genetic algorithm using the Sammon Error Function and the genetic algorithm using the Sum of Squared Error Function.Additionally,the performance of GAk-MEANS is compared with the state-of-the-art statistical unsupervised feature selection techniques.Experimental results show that GAk-MEANS consistently selects subsets of features that result in better classification accuracy compared to others.In particular,GAk-MEANS is able to significantly reduce the size of the subset of selected features by an average of 86.35%(72%–96.14%),which leads to an increase of the accuracy by an average of 3.78%(1.05%–6.32%)compared to using all features.When compared with the genetic algorithm using the Sammon Error Function,GAk-MEANS is able to reduce the size of the subset of selected features by 41.29%on average,improve the accuracy by 5.37%,and reduce the time by 70.71%.When compared with the genetic algorithm using the Sum of Squared Error Function,GAk-MEANS on average is able to reduce the size of the subset of selected features by 15.91%,and improve the accuracy by 9.81%,but the time is increased by a factor of 3.When compared with the machine-learning based methods,we observed that GAk-MEANS is able to increase the accuracy by 13.67%on average with an 88.76%average increase in time. 展开更多
关键词 Genetic algorithm unsupervised feature selection k-means clustering
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Improved k-means clustering algorithm 被引量:16
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作者 夏士雄 李文超 +2 位作者 周勇 张磊 牛强 《Journal of Southeast University(English Edition)》 EI CAS 2007年第3期435-438,共4页
In allusion to the disadvantage of having to obtain the number of clusters of data sets in advance and the sensitivity to selecting initial clustering centers in the k-means algorithm, an improved k-means clustering a... In allusion to the disadvantage of having to obtain the number of clusters of data sets in advance and the sensitivity to selecting initial clustering centers in the k-means algorithm, an improved k-means clustering algorithm is proposed. First, the concept of a silhouette coefficient is introduced, and the optimal clustering number Kopt of a data set with unknown class information is confirmed by calculating the silhouette coefficient of objects in clusters under different K values. Then the distribution of the data set is obtained through hierarchical clustering and the initial clustering-centers are confirmed. Finally, the clustering is completed by the traditional k-means clustering. By the theoretical analysis, it is proved that the improved k-means clustering algorithm has proper computational complexity. The experimental results of IRIS testing data set show that the algorithm can distinguish different clusters reasonably and recognize the outliers efficiently, and the entropy generated by the algorithm is lower. 展开更多
关键词 CLUSTERING k-means algorithm silhouette coefficient
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基于Flink框架的K-means算法优化及并行计算策略 被引量:1
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作者 李召鑫 孟祥印 +2 位作者 肖世德 胡锴沣 赖焕杰 《计算机与数字工程》 2023年第10期2231-2235,共5页
K-means算法因其原理简单和聚类效果尚佳的优点在机器学习和数据挖掘领域得到广泛使用,但其仍存在一些缺点:K-means算法需指定分类类别数K;K-means算法对于初始聚类中心的选取策略是随机选择,这可能会影响到最终聚类结果的准确率及计算... K-means算法因其原理简单和聚类效果尚佳的优点在机器学习和数据挖掘领域得到广泛使用,但其仍存在一些缺点:K-means算法需指定分类类别数K;K-means算法对于初始聚类中心的选取策略是随机选择,这可能会影响到最终聚类结果的准确率及计算速度。以上缺点都限制了K-means算法的计算效率的进一步提升。论文针对以上问题,提出了一种基于Flink并行化的K-means优化算法,该算法在传统K-means算法的基础上引入Canopy算法来完成初始聚类,得到类别数K,然后采用最大距离算法来计算初始聚类中心,并利用Flink框架的并行计算能力,对多个数据集进行聚类实验。实验结果表明,论文算法可以减少聚类过程迭代次数,并且在聚类准确率方面也有一定的提高,在大规模数据集环境下同样具有良好的计算效率。 展开更多
关键词 Flink k-means算法 Canopy算法 并行化
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Application of a Parallel Adaptive Cuckoo Search Algorithm in the Rectangle Layout Problem 被引量:1
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作者 Weimin Zheng Mingchao Si +2 位作者 Xiao Sui Shuchuan Chu Jengshyang Pan 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第6期2173-2196,共24页
The meta-heuristic algorithm is a global probabilistic search algorithm for the iterative solution.It has good performance in global optimization fields such as maximization.In this paper,a new adaptive parameter stra... The meta-heuristic algorithm is a global probabilistic search algorithm for the iterative solution.It has good performance in global optimization fields such as maximization.In this paper,a new adaptive parameter strategy and a parallel communication strategy are proposed to further improve the Cuckoo Search(CS)algorithm.This strategy greatly improves the convergence speed and accuracy of the algorithm and strengthens the algorithm’s ability to jump out of the local optimal.This paper compares the optimization performance of Parallel Adaptive Cuckoo Search(PACS)with CS,Parallel Cuckoo Search(PCS),Particle Swarm Optimization(PSO),Sine Cosine Algorithm(SCA),Grey Wolf Optimizer(GWO),Whale Optimization Algorithm(WOA),Differential Evolution(DE)and Artificial Bee Colony(ABC)algorithms by using the CEC-2013 test function.The results show that PACS algorithmoutperforms other algorithms in 20 of 28 test functions.Due to the superior performance of PACS algorithm,this paper uses it to solve the problem of the rectangular layout.Experimental results show that this scheme has a significant effect,and the material utilization rate is improved from89.5%to 97.8%after optimization. 展开更多
关键词 Rectangular layout cuckoo search algorithm parallel communication strategy adaptive parameter
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An efficient enhanced k-means clustering algorithm 被引量:30
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作者 FAHIM A.M SALEM A.M +1 位作者 TORKEY F.A RAMADAN M.A 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2006年第10期1626-1633,共8页
In k-means clustering, we are given a set of n data points in d-dimensional space R^d and an integer k and the problem is to determine a set of k points in R^d, called centers, so as to minimize the mean squared dista... In k-means clustering, we are given a set of n data points in d-dimensional space R^d and an integer k and the problem is to determine a set of k points in R^d, called centers, so as to minimize the mean squared distance from each data point to its nearest center. In this paper, we present a simple and efficient clustering algorithm based on the k-means algorithm, which we call enhanced k-means algorithm. This algorithm is easy to implement, requiring a simple data structure to keep some information in each iteration to be used in the next iteration. Our experimental results demonstrated that our scheme can improve the computational speed of the k-means algorithm by the magnitude in the total number of distance calculations and the overall time of computation. 展开更多
关键词 Clustering algorithms Cluster analysis k-means algorithm Data analysis
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Estimating wheat fractional vegetation cover using a density peak k-means algorithm based on hyperspectral image data 被引量:4
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作者 LIU Da-zhong YANG Fei-fei LIU Sheng-ping 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2021年第11期2880-2891,共12页
Fractional vegetation cover(FVC)is an important parameter to measure crop growth.In studies of crop growth monitoring,it is very important to extract FVC quickly and accurately.As the most widely used FVC extraction m... Fractional vegetation cover(FVC)is an important parameter to measure crop growth.In studies of crop growth monitoring,it is very important to extract FVC quickly and accurately.As the most widely used FVC extraction method,the photographic method has the advantages of simple operation and high extraction accuracy.However,when soil moisture and acquisition times vary,the extraction results are less accurate.To accommodate various conditions of FVC extraction,this study proposes a new FVC extraction method that extracts FVC from a normalized difference vegetation index(NDVI)greyscale image of wheat by using a density peak k-means(DPK-means)algorithm.In this study,Yangfumai 4(YF4)planted in pots and Yangmai 16(Y16)planted in the field were used as the research materials.With a hyperspectral imaging camera mounted on a tripod,ground hyperspectral images of winter wheat under different soil conditions(dry and wet)were collected at 1 m above the potted wheat canopy.Unmanned aerial vehicle(UAV)hyperspectral images of winter wheat at various stages were collected at 50 m above the field wheat canopy by a UAV equipped with a hyperspectral camera.The pixel dichotomy method and DPK-means algorithm were used to classify vegetation pixels and non-vegetation pixels in NDVI greyscale images of wheat,and the extraction effects of the two methods were compared and analysed.The results showed that extraction by pixel dichotomy was influenced by the acquisition conditions and its error distribution was relatively scattered,while the extraction effect of the DPK-means algorithm was less affected by the acquisition conditions and its error distribution was concentrated.The absolute values of error were 0.042 and 0.044,the root mean square errors(RMSE)were 0.028 and 0.030,and the fitting accuracy R2 of the FVC was 0.87 and 0.93,under dry and wet soil conditions and under various time conditions,respectively.This study found that the DPK-means algorithm was capable of achieving more accurate results than the pixel dichotomy method in various soil and time conditions and was an accurate and robust method for FVC extraction. 展开更多
关键词 fractional vegetation cover k-means algorithm NDVI vegetation index WHEAT
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Polarimetric Meteorological Satellite Data Processing Software Classification Based on Principal Component Analysis and Improved K-Means Algorithm 被引量:1
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作者 Manyun Lin Xiangang Zhao +3 位作者 Cunqun Fan Lizi Xie Lan Wei Peng Guo 《Journal of Geoscience and Environment Protection》 2017年第7期39-48,共10页
With the increasing variety of application software of meteorological satellite ground system, how to provide reasonable hardware resources and improve the efficiency of software is paid more and more attention. In th... With the increasing variety of application software of meteorological satellite ground system, how to provide reasonable hardware resources and improve the efficiency of software is paid more and more attention. In this paper, a set of software classification method based on software operating characteristics is proposed. The method uses software run-time resource consumption to describe the software running characteristics. Firstly, principal component analysis (PCA) is used to reduce the dimension of software running feature data and to interpret software characteristic information. Then the modified K-means algorithm was used to classify the meteorological data processing software. Finally, it combined with the results of principal component analysis to explain the significance of various types of integrated software operating characteristics. And it is used as the basis for optimizing the allocation of software hardware resources and improving the efficiency of software operation. 展开更多
关键词 Principal COMPONENT ANALYSIS Improved k-mean algorithm METEOROLOGICAL Data Processing FEATURE ANALYSIS SIMILARITY algorithm
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Hybrid Genetic Algorithm with K-Means for Clustering Problems 被引量:1
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作者 Ahamed Al Malki Mohamed M. Rizk +1 位作者 M. A. El-Shorbagy A. A. Mousa 《Open Journal of Optimization》 2016年第2期71-83,共14页
The K-means method is one of the most widely used clustering methods and has been implemented in many fields of science and technology. One of the major problems of the k-means algorithm is that it may produce empty c... The K-means method is one of the most widely used clustering methods and has been implemented in many fields of science and technology. One of the major problems of the k-means algorithm is that it may produce empty clusters depending on initial center vectors. Genetic Algorithms (GAs) are adaptive heuristic search algorithm based on the evolutionary principles of natural selection and genetics. This paper presents a hybrid version of the k-means algorithm with GAs that efficiently eliminates this empty cluster problem. Results of simulation experiments using several data sets prove our claim. 展开更多
关键词 Cluster Analysis Genetic algorithm k-means
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Development of slope mass rating system using K-means and fuzzy c-means clustering algorithms 被引量:1
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作者 Jalali Zakaria 《International Journal of Mining Science and Technology》 SCIE EI CSCD 2016年第6期959-966,共8页
Classification systems such as Slope Mass Rating(SMR) are currently being used to undertake slope stability analysis. In SMR classification system, data is allocated to certain classes based on linguistic and experien... Classification systems such as Slope Mass Rating(SMR) are currently being used to undertake slope stability analysis. In SMR classification system, data is allocated to certain classes based on linguistic and experience-based criteria. In order to eliminate linguistic criteria resulted from experience-based judgments and account for uncertainties in determining class boundaries developed by SMR system,the system classification results were corrected using two clustering algorithms, namely K-means and fuzzy c-means(FCM), for the ratings obtained via continuous and discrete functions. By applying clustering algorithms in SMR classification system, no in-advance experience-based judgment was made on the number of extracted classes in this system, and it was only after all steps of the clustering algorithms were accomplished that new classification scheme was proposed for SMR system under different failure modes based on the ratings obtained via continuous and discrete functions. The results of this study showed that, engineers can achieve more reliable and objective evaluations over slope stability by using SMR system based on the ratings calculated via continuous and discrete functions. 展开更多
关键词 SMR based on continuous functions Slope stability analysis k-means and FCM clustering algorithms Validation of clustering algorithms Sangan iron ore mines
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Research on Grid Planning of Dual Power Distribution Network Based on Parallel Ant Colony Optimization Algorithm
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作者 Shuaixiang Wang 《Journal of Electronic Research and Application》 2023年第1期32-41,共10页
A distribution network plays an extremely important role in the safe and efficient operation of a power grid.As the core part of a power grid’s operation,a distribution network will have a significant impact on the s... A distribution network plays an extremely important role in the safe and efficient operation of a power grid.As the core part of a power grid’s operation,a distribution network will have a significant impact on the safety and reliability of residential electricity consumption.it is necessary to actively plan and modify the distribution network’s structure in the power grid,improve the quality of the distribution network,and optimize the planning of the distribution network,so that the network can be fully utilized to meet the needs of electricity consumption.In this paper,a distribution network grid planning algorithm based on the reliability of electricity consumption was completed using ant colony algorithm.For the distribution network structure planning of dual power sources,the parallel ant colony algorithm was used to prove that the premise of parallelism is the interactive process of ant colonies,and the dual power distribution network structure model is established based on the principle of the lowest cost.The artificial ants in the algorithm were compared with real ants in nature,and the basic steps and working principle of the ant colony optimization algorithm was studied with the help of the travelling salesman problem(TSP).Then,the limitations of the ant colony algorithm were analyzed,and an improvement strategy was proposed by using python for digital simulation.The results demonstrated the reliability of model-building and algorithm improvement. 展开更多
关键词 parallel ant colony optimization algorithm Dual power sources Distribution network Grid planning
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PHUI-GA: GPU-based efficiency evolutionary algorithm for mining high utility itemsets
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作者 JIANG Haipeng WU Guoqing +3 位作者 SUN Mengdan LI Feng SUN Yunfei FANG Wei 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2024年第4期965-975,共11页
Evolutionary algorithms(EAs)have been used in high utility itemset mining(HUIM)to address the problem of discover-ing high utility itemsets(HUIs)in the exponential search space.EAs have good running and mining perform... Evolutionary algorithms(EAs)have been used in high utility itemset mining(HUIM)to address the problem of discover-ing high utility itemsets(HUIs)in the exponential search space.EAs have good running and mining performance,but they still require huge computational resource and may miss many HUIs.Due to the good combination of EA and graphics processing unit(GPU),we propose a parallel genetic algorithm(GA)based on the platform of GPU for mining HUIM(PHUI-GA).The evolution steps with improvements are performed in central processing unit(CPU)and the CPU intensive steps are sent to GPU to eva-luate with multi-threaded processors.Experiments show that the mining performance of PHUI-GA outperforms the existing EAs.When mining 90%HUIs,the PHUI-GA is up to 188 times better than the existing EAs and up to 36 times better than the CPU parallel approach. 展开更多
关键词 high utility itemset mining(HUIM) graphics process-ing unit(GPU)parallel genetic algorithm(GA) mining perfor-mance
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Similarity matrix-based K-means algorithm for text clustering
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作者 曹奇敏 郭巧 吴向华 《Journal of Beijing Institute of Technology》 EI CAS 2015年第4期566-572,共7页
K-means algorithm is one of the most widely used algorithms in the clustering analysis. To deal with the problem caused by the random selection of initial center points in the traditional al- gorithm, this paper propo... K-means algorithm is one of the most widely used algorithms in the clustering analysis. To deal with the problem caused by the random selection of initial center points in the traditional al- gorithm, this paper proposes an improved K-means algorithm based on the similarity matrix. The im- proved algorithm can effectively avoid the random selection of initial center points, therefore it can provide effective initial points for clustering process, and reduce the fluctuation of clustering results which are resulted from initial points selections, thus a better clustering quality can be obtained. The experimental results also show that the F-measure of the improved K-means algorithm has been greatly improved and the clustering results are more stable. 展开更多
关键词 text clustering k-means algorithm similarity matrix F-MEASURE
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A Hybrid Method Combining Improved K-means Algorithm with BADA Model for Generating Nominal Flight Profiles
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作者 Tang Xinmin Gu Junwei +2 位作者 Shen Zhiyuan Chen Ping Li Bo 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2016年第4期414-424,共11页
A high-precision nominal flight profile,involving controllers′intentions is critical for 4Dtrajectory estimation in modern automatic air traffic control systems.We proposed a novel method to effectively improve the a... A high-precision nominal flight profile,involving controllers′intentions is critical for 4Dtrajectory estimation in modern automatic air traffic control systems.We proposed a novel method to effectively improve the accuracy of the nominal flight profile,including the nominal altitude profile and the speed profile.First,considering the characteristics of trajectory data,we developed an improved K-means algorithm.The approach was to measure the similarity between different altitude profiles by integrating the space warp edit distance algorithm,thereby to acquire several fitted nominal flight altitude profiles.This approach breaks the constraints of traditional K-means algorithms.Second,to eliminate the influence of meteorological factors,we introduced historical gridded binary data to determine the en-route wind speed and temperature via inverse distance weighted interpolation.Finally,we facilitated the true airspeed determined by speed triangle relationships and the calibrated airspeed determined by aircraft data model to extract a more accurate nominal speed profile from each cluster,therefore we could describe the airspeed profiles above and below the airspeed transition altitude,respectively.Our experimental results showed that the proposed method could obtain a highly accurate nominal flight profile,which reflects the actual aircraft flight status. 展开更多
关键词 air transportation flight profile k-means algorithm space warp edit distance(SWED)algorithm trajectory prediction
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An Improved K-Means Algorithm Based on Initial Clustering Center Optimization
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作者 LI Taihao NAREN Tuya +2 位作者 ZHOU Jianshe REN Fuji LIU Shupeng 《ZTE Communications》 2017年第B12期43-46,共4页
The K-means algorithm is widely known for its simplicity and fastness in text clustering.However,the selection of the initial clus?tering center with the traditional K-means algorithm is some random,and therefore,the ... The K-means algorithm is widely known for its simplicity and fastness in text clustering.However,the selection of the initial clus?tering center with the traditional K-means algorithm is some random,and therefore,the fluctuations and instability of the clustering results are strongly affected by the initial clustering center.This paper proposed an algorithm to select the initial clustering center to eliminate the uncertainty of central point selection.The experiment results show that the improved K-means clustering algorithm is superior to the traditional algorithm. 展开更多
关键词 CLUSTERING k-means algorithm initial clustering center
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A State of Art Analysis of Telecommunication Data by k-Means and k-Medoids Clustering Algorithms
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作者 T. Velmurugan 《Journal of Computer and Communications》 2018年第1期190-202,共13页
Cluster analysis is one of the major data analysis methods widely used for many practical applications in emerging areas of data mining. A good clustering method will produce high quality clusters with high intra-clus... Cluster analysis is one of the major data analysis methods widely used for many practical applications in emerging areas of data mining. A good clustering method will produce high quality clusters with high intra-cluster similarity and low inter-cluster similarity. Clustering techniques are applied in different domains to predict future trends of available data and its uses for the real world. This research work is carried out to find the performance of two of the most delegated, partition based clustering algorithms namely k-Means and k-Medoids. A state of art analysis of these two algorithms is implemented and performance is analyzed based on their clustering result quality by means of its execution time and other components. Telecommunication data is the source data for this analysis. The connection oriented broadband data is given as input to find the clustering quality of the algorithms. Distance between the server locations and their connection is considered for clustering. Execution time for each algorithm is analyzed and the results are compared with one another. Results found in comparison study are satisfactory for the chosen application. 展开更多
关键词 k-means algorithm k-Medoids algorithm DATA CLUSTERING Time COMPLEXITY TELECOMMUNICATION DATA
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Multiple Parameter Based Clustering (MPC): Prospective Analysis for Effective Clustering in Wireless Sensor Network (WSN) Using K-Means Algorithm
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作者 Md. Asif Khan Israfil Tamim +1 位作者 Emdad Ahmed M. Abdul Awal 《Wireless Sensor Network》 2012年第1期18-24,共7页
In wireless sensor network cluster architecture is useful because of its inherent suitability for data fusion. In this paper we represent a new approach called Multiple Parameter based Clustering (MPC) embedded with t... In wireless sensor network cluster architecture is useful because of its inherent suitability for data fusion. In this paper we represent a new approach called Multiple Parameter based Clustering (MPC) embedded with the traditional k-means algorithm which takes different parameters (Node energy level, Euclidian distance from the base station, RSSI, Latency of data to reach base station) into consideration to form clusters. Then the effectiveness of the clusters is evaluated based on the uniformity of the node distribution, Node range per cluster, Intra and Inter cluster distance and required energy level of each centroid. Our result shows that by varying multiple parameters we can create clusters with more uniformly distributed nodes, minimize intra and maximize inter cluster distance and elect less power consuming centroid. 展开更多
关键词 k-means algorithm Energy Efficient UNIFORM Distribution RSSI LATENCY
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一种K-means改进算法的并行化实现与应用 被引量:50
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作者 李晓瑜 俞丽颖 +1 位作者 雷航 唐雪飞 《电子科技大学学报》 EI CAS CSCD 北大核心 2017年第1期61-68,共8页
随着数据的爆炸式增长,聚类研究作为大数据的核心问题之一,正面临计算复杂度高和计算能力不足等诸多问题。提出了一种基于Hadoop的分布式改进K-means算法,该算法通过引入Canopy算法初始化K-means算法的聚类中心,克服传统K-means算法因... 随着数据的爆炸式增长,聚类研究作为大数据的核心问题之一,正面临计算复杂度高和计算能力不足等诸多问题。提出了一种基于Hadoop的分布式改进K-means算法,该算法通过引入Canopy算法初始化K-means算法的聚类中心,克服传统K-means算法因初始中心点的不确定性,易陷入局部最优解的问题。本算法在Canopy(罩盖)中完成K-means聚类,并在Canopy间完成簇的合并,聚类效果稳定,迭代次数少。同时,结合MapReduce分布式计算模型,给出改进后算法的并行化设计方法和策略,进一步通过改进相似度度量方法,将该方法用于文本聚类中。实验结果证明该算法具有良好的准确率和扩展性。 展开更多
关键词 canopy算法 HADOOP MAPREDUCE 并行K—means 文本聚类
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