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Suitable error evaluation criteria selection in the wind energy assessment via the k-means clustering algorithm

INTERNATIONAL JOURNAL OF GREEN ENERGY 2016年第11期13卷 1145-1162页

作者： Wu, Jie Wang, Jianzhou Qin, Shanshan Lu, Haiyan Northwest Univ Nationalities Sch Math & Comp Sci Lanzhou Peoples R China Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China Lanzhou Univ Res Sch Arid Environm & Climate Change MOE Key Lab Western Chinas Environm Syst Lanzhou Peoples R China Univ Technol Sydney Fac Engn & Informat Technol Sydney NSW Australia

In this paper, wind energy potential of four locations in Xinjiang region is assessed. The Weibull distribution as well as the Logistic and the Lognormal distributions are applied to describe the distributions of the wind speed at different heights. In determining the parameters in the Weibull distribution, four intelligent parameter optimization approaches including the differential evolutionary, the particle swarm optimization, and two other approaches derived from these two algorithms and combined advantages of these two approaches are employed. Then the optimal distribution is chosen through the Chi-square error (CSE), the kolmogorov-Smirnov test error (kSE), and the root mean square error (RMSE) criteria. However, it is found that the variation range of some criteria is quite large, thus these criteria are analyzed and evaluated both from the anomalous values and by the k-means clustering method. Anomaly observation results have shown that the CSE is the first one should be considered to be eliminated from the consequent optimal distribution function selection. This idea is further confirmed by the k-means clustering algorithm, by which the CSE is clustered into a different group with kSE and RMSE. Therefore, only the reserved two error evaluation criteria are utilized to evaluate the wind power potential.

关键词： Anomalous value k-means clustering algorithm parameter optimization wind energy wind power classification

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Recognition and localization of occluded apples using k-means clustering algorithm and convex hull theory: a comparison

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MULTIMEDIA TOOLS AND APPLICATIONS 2016年第6期75卷 3177-3198页

作者： Wang, Dandan Song, Huaibo Tie, Zhihui Zhang, Weiyuan He, Dongjian Northwest A&F Univ Coll Mech & Elect Engn Yangling 712100 Shaanxi Peoples R China

For apple harvesting robot, it is difficult to acquire the coordinates of occluded apples accurately in natural scenes, which is important in implementing picking tasks. In this paper, a method on automatic recognition and localization of occluded apples was proposed. Firstly, an apple recognition algorithm based on k-means clustering theory was described. Secondly, convex hull information which was obtained by following the contours of extracted apple regions was used to extract the real apple edges. Finally, three points from these real edges were selected to estimate the centers and radius of apples. This algorithm was tested and compared with traditional Hough transform method (HT method) and contour curvature method (CC method) and 125 apple images were used to test the effectiveness of these methods. Four parameters including Segmentation Error (SE), False Positive Rate (FPR), False Negative Rate (FNR) and Overlap Index (OI) were used to evaluate the performance of these methods. Experimental results showed that SE of the presented method was decreased by 14.399 and 30.782 % when compared to CC method and HT method respectively, FPR by 7.234 and 11.728 % and OI was increased by 18.644 and 30.938 %. FNR of the proposed method was 0.912 % lower than CC method, while it was 5.869 % higher than HT method. The experimental results indicated that the proposed method could get much better localization rate than Hough transform method and contour curvature method, thus it could be concluded that the algorithm is an efficient means for the recognition and localization of occluded apples.

关键词： Occluded apple Localization Recognition k-means clustering algorithm Convex hull

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Identification of new candidate drugs for lung cancer using chemical-chemical interactions, chemical-protein interactions and a k-means clustering algorithm

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JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS 2016年第4期34卷 906-917页

作者： Lu, Jing Chen, Lei Yin, Jun Huang, Tao Bi, Yi kong, Xiangyin Zheng, Mingyue Cai, Yu-Dong Yantai Univ Collaborat Innovat Ctr Adv Drug Delivery Syst & B Minist Educ Sch PharmKey Lab Mol Pharmacol & Drug Evaluat Yantai 264005 Peoples R China Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China Shanghai Jiao Tong Univ Sch Med SJTUSM Inst Hlth Sci Key Lab Stem Cell Biol Shanghai 200025 Peoples R China Chinese Acad Sci Shanghai Inst Biol Sci Shanghai 200025 Peoples R China Shanghai Inst Mat Med Drug Discovery & Design Ctr Shanghai 201203 Peoples R China Shanghai Univ Coll Life Sci Shanghai 200444 Peoples R China

Lung cancer, characterized by uncontrolled cell growth in the lung tissue, is the leading cause of global cancer deaths. Until now, effective treatment of this disease is limited. Many synthetic compounds have emerged with the advancement of combinatorial chemistry. Identification of effective lung cancer candidate drug compounds among them is a great challenge. Thus, it is necessary to build effective computational methods that can assist us in selecting for potential lung cancer drug compounds. In this study, a computational method was proposed to tackle this problem. The chemical-chemical interactions and chemical-protein interactions were utilized to select candidate drug compounds that have close associations with approved lung cancer drugs and lung cancer-related genes. A permutation test and k-means clustering algorithm were employed to exclude candidate drugs with low possibilities to treat lung cancer. The final analysis suggests that the remaining drug compounds have potential anti-lung cancer activities and most of them have structural dissimilarity with approved drugs for lung cancer.

关键词： chemical-protein interaction k-means clustering algorithm lung cancer chemical-chemical interaction

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Researches on the Calculation Method of Saturation Flow Rate at Signalized Intersection Based on k-means clustering algorithm

Researches on the Calculation Method of Saturation Flow Rate...

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International Conference on Computer Technology, Electronics and Communication

作者： Weiming Luo Jianhua Yuan Qing Ma Lin Xu Traffic Management Research Institute of Ministry of Public Security Ministry Wuxi Jiangsu China

ISBN: (纸本)9781538657850

This paper studies k-means clustering algorithm for the calculation of saturation flow rate at signalized intersection. The calculation results are based on the measured traffic data, which are surveyed at the intersection of Xinhua Road and Youyi Road in Tangshan. By analyzing the distribution characteristics of average headway in a day, this paper presents the negative influences of unsaturated or oversaturated traffic flow on headway. A portion of the average headway which affected by unsaturated or oversaturated condition are removed by k-means clustering algorithm. The developed method is used to calculate the saturation flow rate within different conditions. The calculation results illustrate that the proposed method can offset the effects of unsaturated or oversaturated condition.

关键词： The saturation flow rate Headway Signalized intersection k-means clustering algorithm

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Open cluster membership probability based on k-means clustering algorithm

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EXPERIMENTAL ASTRONOMY 2016年第1期42卷 49-59页

作者： Abd El Aziz, Mohamed Selim, I. M. Essam, A. Zagazig Univ Dept Math Fac Sci Zagazig 44519 Egypt Nahda Univ Fac Comp Sci Bani Suwayf Egypt Natl Res Inst Astron & Geophys Dept Astron Cairo Egypt HTI Cairo Egypt

In the field of galaxies images, the relative coordinate positions of each star with respect to all the other stars are adapted. Therefore the membership of star cluster will be adapted by two basic criterions, one for geometric membership and other for physical (photometric) membership. So in this paper, we presented a new method for the determination of open cluster membership based on k-means clustering algorithm. This algorithm allows us to efficiently discriminate the cluster membership from the field stars. To validate the method we applied it on NGC 188 and NGC 2266, membership stars in these clusters have been obtained. The color-magnitude diagram of the membership stars is significantly clearer and shows a well-defined main sequence and a red giant branch in NGC 188, which allows us to better constrain the cluster members and estimate their physical parameters. The membership probabilities have been calculated and compared to those obtained by the other methods. The results show that the k-means clustering algorithm can effectively select probable member stars in space without any assumption about the spatial distribution of stars in cluster or field. The similarity of our results is in a good agreement with results derived by previous works.

关键词： Galaxy Open clusters and associations Individual k-means clustering algorithm NGC 2266 NGC 188 Color-magnitude diagram (CMD)

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A Prediction Model of Hard landing Based on RBF Neural Network with k-means clustering algorithm

A Prediction Model of Hard landing Based on RBF Neural Netwo...

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IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

作者： Qiao, Xiaoduo Chang, Wenbing Zhou, Shenghan Lu, Xuefeng Beihang Univ Sch Reliabil & Syst Engn Beijing Peoples R China

ISBN: (纸本)9781509036653

This paper proposes a prediction model for forecasting the hard landing problem. The landing phase has been demonstrated the most dangerous phase in flight cycle for fatal accidents. The landing safety problem has become one of the hot research problems in engineering management field. The study concentrates more on the prediction and advanced warning of hard landing. Firstly, flight data is preprocessed with data slicing method based on flight height and dimension reduction. Subsequently, the radial basis function (RBF) neural network model is established to predict the hard landing. Then, the structure parameters of the model are determined by the k-means clustering algorithm. In the end, compared with Support Vector Machine and BP neural network, the RBF neural network based on k-means clustering algorithm model is adopted and the prediction accuracy of hard landing is better than traditional ways.

关键词： flight data hard landing prediction model RBF neural network k-means clustering algorithm

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k-means clustering algorithm for Large-scale Chinese Commodity Information Web Based on Hadoop 14

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

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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

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

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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

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

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

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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

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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