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IEEE International Conference on Electrical Engineering, Big Data and algorithms (EEBDA)

作者： Li, Meihua Wu, keqing Chen, Le Jiangxi Univ Sci & Technol Sch Sci Ganzhou Peoples R China Jiangxi Univ Sci & Technol Educ Informat Technol Ctr Ganzhou Peoples R China

ISBN: (纸本)9781665416061

Sina Weibo, as a platform for netizens to express their opinions, generates a large amount of public opinion data and constantly generates new topics. How to detect new and hot topics on Weibo is a meaningful studied issue. Document Clustering is a widely studied problem in Text Categorization. k-means is one of the most famous unsupervised learning algorithms, partitions a given dataset into disjoint clusters following a simple and easy way. But the traditional k-means algorithm assigns initial centroids randomly, which cannot guarantee to choose the maximum dissimilar documents as the centroids for the clusters. A modified k-means algorithm is proposed, which uses Jaccard distance measure for assigningthe most dissimilar k documents as centroids, and uses Word2vec as the Chinese text vectorization model. Theexperimental results demonstrate that the proposed k-means algorithm improves the clustering performance, and is able to detect new and hot topics based on Weibo COVID-19 data.

关键词： k-means algorithm Word2vec Weibo topic detection

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EVRP: A novel geometrical based energy efficient eye vision routing protocol for wireless sensor networks based on the k-means algorithm

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AD HOC NETWORkS 2024年 160卷

作者： Abdou, Mohamed Amer, Hanan M. Abdelsalam, Mohamed M. khalil, Abeer T. Mansoura Univ Fac Engn Elect & Commun Engn Dept Mansoura Egypt Mansoura Univ Fac Engn Comp Engn & Control Syst Dept Mansoura Egypt

The limited energy source used in wireless sensor networks (WSNs) is a crucial aspect when designing a routing protocol. Developing a routing protocol that tries to maximize energy utilization can significantly improve the system's lifetime and balance energy consumption. A novel geometrical-based energy-efficient routing protocol called "EVRP" is presented in this paper. The Cluster generation process is based on the k-means algorithm for nodes grouping. To choose a cluster head (CH) for each cluster, a cost function is calculated for each node considering factors such as remaining energy level for the node, and the mean distance between the node and its neighbors. In the inter-cluster routing phase, a novel geometrical-based routing protocol is introduced considering factors such as the distance to a base station (BS), remaining energy level, and distance between CHs to find the optimal path between BS and CH. The cluster structure is integrated with direct communication in addition to a dynamic clustering mechanism based on the optimum number of CHs and a load balancing mechanism to reduce the load on CHs near BS. The simulation results demonstrate that in different scenarios, the proposed protocol can significantly increase the network lifetime, network throughput, and network stability by 52 %, 45 %, and 30 % respectively compared with the EECRAIFA protocol and by 100 %, 97.6 %, 64.3 % respectively compared with D2CRP protocol.

关键词： Energy-efficiency k-means algorithm Routing protocols Wireless sensor networks

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k-NNDP: k-means algorithm based on nearest neighbor density peak optimization and outlier removal

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kNOWLEDGE-BASED SYSTEMS 2024年 294卷

作者： Liao, Jiyong Wu, Xingjiao Wu, Yaxin Shu, Juelin Huaihua Univ Sch Comp & Artificial Intelligence Sch Software Huaihua 418000 Peoples R China Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China East China Normal Univ Shanghai Key Lab Multidimens Informat Proc Shanghai 200062 Peoples R China Harbin Inst Technol Sch Transportat Sci & Engn Harbin 150090 Peoples R China CLP Big Data Res Inst Co Ltd Guiyang 550002 Peoples R China

k -means is an unsupervised method for vector quantification derived from signal processing. It is currently used in data mining and knowledge -discovery. The advantages of k -means include its simple operation, scalability, and suitability for processing large-scale datasets. However, k -means randomly selects the initial cluster center, which causes unstable clustering results, and outliers affect algorithm performance. To address this challenge, we propose a nearest -neighbor density peak (NNDP)-optimized initial cluster center and outlier removal algorithm. To solve the problem of randomly selecting the initial cluster center, we propose NNDP-based k -means (k-NNDP). k-NNDP automatically selects the initial cluster centers based on decision values, ensuring stable algorithm operation. In addition, we adopt a local search strategy to eliminate outliers, identify outliers using a set threshold, and use the median instead of the mean in subsequent centroid iterations to reduce the impact of outliers on the algorithm. It is worth mentioning that, to date, most previous studies have addressed the two problems independently, which makes it easy for the algorithm to fall into a local optimal solution. Therefore, we innovatively combine these two problems using k -nearest neighbor modeling. To evaluate the effectiveness of k-NNDP, we conducted comparative experiments on several synthetic and real -world datasets. k-NNDP outperformed two classical algorithms and six state-of-the-art improved k -means algorithms. The results prove that k-NNDP can effectively solve the problems of randomness and outlier influence of k -means, and the effect is significant.

关键词： Clustering k-means algorithm Nearest neighbor density peak Initial cluster center Outlier detection

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Research on Oil and Gas-Bearing Zone Prediction and Identification Based on the SVD-k-means algorithm-A Case Study of the WZ6-1 Oil-Bearing Structure in the Beibu Gulf Basin, South China Sea

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ENERGIES 2024年第22期17卷 5771页

作者： Chen, Zhilong Wang, Renyi Xu, Biao Zhu, Jianghang Zhejiang Ocean Univ Sch Petrochem Engn & Environm Zhoushan 316022 Peoples R China

The WZ6-1 oil-bearing structure in the Beibu Gulf Basin of the South China Sea has well-developed faults with significant variations in fault sealing capacity, resulting in a complex and highly variable distribution of oil, gas, and water, and limited understanding of hydrocarbon accumulation patterns. Traditional methods, such as single seismic attributes and linear fusion of multiple seismic attributes, have proven ineffective in identifying and predicting oil and gas-bearing areas in this region, leading to five unsuccessful wells. Through comprehensive analysis of drilled wells and seismic data, six types of horizon seismic attributes were selected. A novel approach for predicting oil-bearing zones was proposed, employing SVD-k-means nonlinear clustering for multiple seismic attribute fusion. The application results indicate: (1) Singular value decomposition (SVD) technology not only reduces the correlation redundancy among seismic attribute data variables, but enables data dimensionality reduction and noise suppression, decreasing ambiguity in prediction results and enhancing reliability. (2) The k-means nonlinear clustering method facilitates the nonlinear fusion of multiple seismic attribute parameters, effectively uncovering the nonlinear features of the underlying relationship between seismic attributes and reservoir oil-bearing characteristics, thereby amplifying the hydrocarbon information within the seismic attribute variables. (3) Compared to k-means, SVD-k-means demonstrates superior performance across all metrics, with an 18.4% increase in the SC coefficient, a 57.8% increase in the CH index, and a 24.7% improvement in the DB index. (4) The results of oil-bearing zone prediction using the SVD-k-means algorithm align well with the drilling outcomes in the study area and correspond to the geological patterns of hydrocarbon enrichment in this region. This has been confirmed by the high-yield industrial oil flow obtained from the newly drilled WZ6-1-A3 we

关键词： seismic attributes k-means algorithm singular value decomposition petroliferous area prediction

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Research on accurate marketing of e-commerce based on k-means algorithm

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Procedia Computer Science 2025年 261卷 1208-1214页

作者： Xinyi Wang Xiaoling Liao School of Environment Education and Development The University of Manchester Manchester M139PL UK School of foreign language Jinggangshan University Jian 343009 China

In recent years, e-commerce accurate marketing has attracted more and more attention. In accurate marketing, accurate calculation of user purchase behavior is the most important link. However, due to the diversity, complexity and variability of user data, traditional technical means are difficult to deal with, while data mining technology can help process data and extract effective information from these massive data, so this technology has received high attention in the field of e-commerce precision marketing. In this paper, an accurate marketing method of e-commerce based on k-means algorithm is proposed, hoping to provide a more accurate and intelligent marketing strategy for enterprises.

关键词： k-means algorithm E-commerce marketing Precision marketing

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Optimisation of k-means algorithm based on sample density canopy

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INTERNATIONAL JOURNAL OF AD HOC AND UBIQUITOUS COMPUTING 2021年第1-3期38卷 62-69页

作者： Shen, Guo-xin Jiang, Zhong-yun Shanghai Ocean Univ Informat Inst Shanghai 210306 Peoples R China Shanghai Jianqiao Univ Informat Technol Inst Shanghai 210306 Peoples R China

Since the random selection of the initial centroid and the artificial definition of the number of clusters affect the experimental results of k-means, therefore, this article uses sample density and canopy to optimise the k-means algorithm. This algorithm first calculates the sample density of each data, and selects the data point with the smallest density as the first cluster centroid;then combines the canopy algorithm to cluster the original sample data to obtain the number of clusters and each cluster centre. As initial parameter of the k-means finally combines the k-means algorithm to assemble the original samples, UCI dataset and self-built dataset were used to compare simulation experiments. The results show that the algorithm can make clustering results more accurate, run faster, and improve the stability of the algorithm.

关键词： clustering k-means algorithm density neighbourhood initial centroid

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A Novel Industry-Classification Final Energy Consumption Structure Clustering Method Based on Improved k-means algorithm 4

A Novel Industry-Classification Final Energy Consumption Str...

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4th Asia Energy and Electrical Engineering Symposium (AEEES)

作者： Zhao, Zilong Tang, Jinrui Liu, Jianchao Ge, Ganheng Yang, Honghui Wuhan Univ Technol Sch Automat Wuhan Peoples R China

ISBN: (纸本)9781665479141

The industry-classification final energy consumption structure is inextricably linked to economic development. Due to the goal of carbon neutrality, China's industry-classification final energy consumption structure is undergoing profound changes. It is challenging to analyze the industry-classification final energy consumption structure in multiple dimensions using analytical tools such as line charts. In order to illustrate the variation of the final energy consumption structure in different industry sectors at different times, a novel industry-classification final energy consumption structure clustering method based on an improved k-means algorithm is proposed in this paper. Three methods, including the elbow method, the silhouette coefficient method, and the Calinski-Harabasz (CH) index method, are used to optimize k values in the k-means algorithm. The classification results are evaluated through the empirical analyses from the China industry dataset. The simulation results demonstrate that the proposed method can accurately classify the industry-classification final energy consumption structure of industrial sub-sectors into four categories. Moreover, the trend of the evolution of the industry consumption structure shows that most industries have shifted from coal-based consumption to electricity-based consumption.

关键词： industry-classification energy consumption structure k-means algorithm elbow method silhouette coefficient method Calinski-Harabasz index method

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Estimating wheat fractional vegetation cover using a density peak k-means algorithm based on hyperspectral image data

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Journal of Integrative Agriculture 2021年第11期20卷 2880-2891页

作者： LIU Da-zhong YANG Fei-fei LIU Sheng-ping Key Laboratory of Agricultural Information Service Technology Ministry of Agriculture and Rural Affairs/Intelligent Agriculture Research OfficeAgricultural Information InstituteChinese Academy of Agricultural SciencesBeijing 100081P.R.China

Fractional vegetation cover(FVC)is an important parameter to measure crop *** studies of crop growth monitoring,it is very important to extract FVC quickly and *** the most widely used FVC extraction method,the photographic method has the advantages of simple operation and high extraction ***,when soil moisture and acquisition times vary,the extraction results are less *** 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)*** this study,Yangfumai 4(YF4)planted in pots and Yangmai 16(Y16)planted in the field were used as the research *** 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 *** 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 *** 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 *** 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 *** 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,*** study found that the DPk-means algorithm was capable of achieving more accur

关键词： fractional vegetation cover k-means algorithm NDVI vegetation index wheat

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An improved k-means algorithm for underwater image background segmentation

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第14期80卷 21059-21083页

作者： Chen, Wei He, Cenyu Ji, Chunlin Zhang, Meiying Chen, Siyu Nanjing Inst Technol Coll Innovat & Entrepreneurship Ind Ctr Nanjing 211167 Peoples R China Shenzhen Kuang Chi Space Technol Co Ltd Shenzhen 518000 Peoples R China Shenzhen Kuang Chi Inst Adv Technol Shenzhen 518000 Peoples R China Tech Univ Munich Dept Elect & Comp Engn D-80333 Munich Germany

Conventional algorithms fail to obtain satisfactory background segmentation results for underwater images. In this study, an improved k-means algorithm was developed for underwater image background segmentation to address the issue of improper k value determination and minimize the impact of initial centroid position of grayscale image during the gray level quantization of the conventional k-means algorithm. A total of 100 underwater images taken by an underwater robot were sampled to test the aforementioned algorithm in respect of background segmentation validity and time cost. The k value and initial centroid position of grayscale image were optimized. The results were compared to the other three existing algorithms, including the conventional k-means algorithm, the improved Otsu algorithm, and the Canny operator edge extraction method. The experimental results showed that the improved k-means underwater background segmentation algorithm could effectively segment the background of underwater images with a low color cast, low contrast, and blurred edges. Although its cost in time was higher than that of the other three algorithms, it none the less proved more efficient than the time-consuming manual segmentation method. The algorithm proposed in this paper could potentially be used in underwater environments for underwater background segmentation.

关键词： Underwater image Image segmentation Background segmentation k-means algorithm

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Research on fast identification technology of forged fingerprints based on the improved k-means algorithm

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INTERNATIONAL JOURNAL OF BIOMETRICS 2021年第1期13卷 17-29页

作者： Ren, Zhao-ting Northwest Minzu Univ Dept Expt Teaching Lanzhou 730030 Peoples R China

In order to overcome the low accuracy of the traditional method, a fast identification method based on the improved k-mean algorithm is proposed. Spatial grid block model is constructed to extract the fingerprint texture features and then the fingerprint profile features are detected using the edge outline extraction method. The kalman fusion method is used to reconstruct fingerprint information. Using the neighbourhood distributed retrieval method, fingerprint image feature fusion is realised and the texture feature extraction model for forged fingerprints is established. The k-means clustering method is used for fingerprint feature clustering to realise fast identification of forged fingerprints. Experimental results show that the identification accuracy of this method is higher than 0.85, and the identification stability is good. The signal-to-noise ratio of fingerprint images is always between 25.3 dB and 82.3 dB, and the imaging quality is high, indicating that this method can realise fast and accurate identification of forged fingerprints.

关键词： k-means algorithm forged fingerprint fast identification feature extraction texture

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