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A novel rough semi-supervised k-means algorithm for text clustering

INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2023年第2期21卷 57-68页

作者： Tang, Lei-yu Wang, Zhen-hao Wang, Shu-dong Fan, Jian-cong Yue, Guo-wei Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Peoples R China China Univ Petr Coll Comp Sci & Technol Qingdao Peoples R China Shandong Univ Sci & Technol Key Lab Min Disaster Prevent & Control Qingdao 266590 Peoples R China

Since many attribute values of high-dimensional sparse data are zero, we combine the approximation set of the rough set theory with the semi-supervised k-means algorithm to propose a rough set-based semi-supervised k-means (RSkmeans) algorithm. Firstly, the proportion of non-zero values is calculated by a few labelled data samples, and a small number of important attributes in each cluster are selected to calculate the clustering centres. Secondly, the approximation set is used to calculate the information gain of each attribute. Thirdly, different attribute values are partitioned into the corresponding approximate sets according to the comparison of information gain with the upper approximation and boundary threshold. Then, the new attributes are increased and the above process is continued to update the clustering centres. The experimental results on text data show that the RSkmeans algorithm can help find the important attributes, filter the invalid information, and improve the performances significantly.

关键词： rough set approximation set k-means algorithm semi-supervised clustering high dimensional sparse data

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Simulation of Rosette scanning infrared seeker and counter-countermeasure using k-means algorithm

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 1999年第6期E82A卷 987-993页

作者： Jahng, SG Hong, Hk Choi, JS Chung Ang Univ Dept Elect Engn Seoul 156756 South Korea

The rosette-scanning infrared seeker (RSIS) is a tracker that a single infrared detector scans the total field of view (TFOV) in a rosette pattern, and then produces 2D image about a target. Since the detected image has various shapes in accordance with the target position in the TFOV, it is difficult to determine a precise target position from the obtained image. In order to track this type of target, therefore, we propose an efficient tracking method using the k-means algorithm (kMA). The kMA, which classifies image clusters and calculates their centers, is used to cope with an countermeasure (CM) such as an IR flare. To evaluate the performance of the RSIS using the kMA dynamically, we simulate the RSIS in the various conditions, and discuss the tracking results.

关键词： Rosette scan seeker counter-countermeasure countermeasure k-means algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第26期81卷 37007-37032页

作者： Gupta, Manoj kumar Chandra, Pravin Guru Gobind Singh Indraprastha Univ Univ Sch Informat & Commun Technol Sect 16C Delhi 110078 India Trinity Inst Innovat Profess Studies Greater Noida 201310 Uttar Pradesh India

Recently, Internet of Things (IoT) and multimedia are gaining popularity because of their usages in various applications. Numerous sensors and automated devices are generating huge volumes of data. Therefore, it is required to efficiently and effectively analyze this voluminous data. It can be achieved by using appropriate machine leaning techniques such as clustering. Among the clustering techniques, the k-means method/algorithm is one of the simplest, effective and commonly used methods. For making the cluster, it uses a measure of similarity/distance among the data observations. Nearby/similar data observations are placed within the same cluster whereas distant/dis-similar data observations are placed in other clusters. Hence, the similarity/distance metric plays a major role on the performance and accuracy of the k-means. Therefore, using an appropriate similarity/distance metric, the performance and accuracy of the k-means can be improved. k-means algorithm is majorly implemented using Euclidean distance metric. With the objective to explore the better and/or alternate similarity/distance metric(s) for k-means, a case study, based on empirical evaluation, of thirteen different similarity/distance metrics on six well-known datasets is performed and presented in this paper. By using the efficient and effective similarity / distance metrics, the performance and accuracy of the k-means algorithm can be improved which leads to formation of good clusters of various data observations or things or images etc. The results of the empirical study are analyzed and compared on the basis of widely used statistical clustering evaluation/validation measures. Based on the comparative results, these metrics are assigned with the ranks. Overall, the results demonstrate that Manhattan and Minkowski distance metrics gives better results for k-means algorithm.

关键词： Internet of things Data analytics and multimedia Similarity metrics Distance metrics k-means algorithm Clustering Machine learning

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Improved k-means algorithm based on density Canopy

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kNOWLEDGE-BASED SYSTEMS 2018年 145卷 289-297页

作者： Zhang, Geng Zhang, Chengchang Zhang, Huayu China Elect Power Res Inst Informat & Commun Dept Beijing Peoples R China Chongqing Univ Posts & Telecommun Coll Elect Engn Chongqing Peoples R China

In order to improve the accuracy and stability of k-means algorithm and solve the problem of determining the most appropriate number k of clusters and best initial seeds, an improved k-means algorithm based on density Canopy is proposed. Firstly, the density of sample data sets, the average sample distance in clusters and the distance between clusters are calculated, choosing the density maximum sampling point as the first cluster center and removing the density cluster from the data sets. Defining the product of sample density, the reciprocal of the average distance between the samples in the cluster, and the distance between the clusters as weight product, the other initial seeds is determined by the maximum weight product in the remaining data sets until the data sets is empty. The density Canopy is used as the preprocessing procedure of k-means and its result is used as the cluster number and initial clustering center of k-means algorithm. Finally, the new algorithm is tested on some well-known data sets from UCI machine learning repository and on some simulated data sets with different proportions of noise samples. The simulation results show that the improved k-means algorithm based on density Canopy achieves better clustering results and is insensitive to noisy data compared to the traditional k-means algorithm, the Canopy-based k-means algorithm, Semi-supervised k-means++ algorithm and k-means u* algorithm. The clustering accuracy of the proposed k-means algorithm based on density Canopy is improved by 30.7%, 6.1%, 5.3% and 3.7% on average on UCI data sets, and improved by 44.3%, 3.6%, 9.6% and 8.9% on the simulated data sets with noise signal respectively. With the increase of the noise ratio, the noise immunity of the new algorithm is more obvious, when the noise ratio reached 30%, the accuracy rate is improved 50% and 6% compared to the traditional k-means algorithm and the Canopy-based k-means algorithm. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Density Canopy k-means algorithm Weight product Optimal value k Initial seed

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An evolutionary technique based on k-means algorithm for optimal clustering in R^N

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INFORMATION SCIENCES 2002年第1-4期146卷 221-237页

作者： Bandyopadhyay, S Maulik, U Indian Stat Inst Inst Machine Intelligence Kolkata 700108 W Bengal India Kalyani Govt Engn Coll Dept Comp Sci & Engn Kalyani Nadia India

A genetic algorithm-based efficient clustering technique that utilizes the principles of k-means algorithm is described in this paper. The algorithm called kGA-clustering, while exploiting the searching capability of k-means, avoids its major limitation of getting stuck at locally optimal values. Its superiority over the k-means algorithm and another genetic algorithm-based clustering method, is extensively demonstrated for several artificial and real life data sets. A real life application of the kGA-clustering in classifying the pixels of a satellite image of a part of the city of Mumbai is provided. (C) 2002 Elsevier Science Inc. All rights reserved.

关键词： clustering genetic algorithms k-means algorithm satellite image classification

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A comparative study of the k-means algorithm and the normal mixture model for clustering: Bivariate homoscedastic case

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JOURNAL OF STATISTICAL PLANNING AND INFERENCE 2010年第7期140卷 1701-1711页

作者： Qiu, Dingxi Univ Miami Dept Ind Engn Coral Gables FL 33146 USA

The k-means algorithm and the normal mixture model method are two common clustering methods. The k-means algorithm is a popular heuristic approach which gives reasonable clustering results if the component clusters are ball-shaped. Currently, there are no analytical results for this algorithm if the component distributions deviate from the ball-shape. This paper analytically studies how the k-means algorithm changes its classification rule as the normal component distributions become more elongated under the homoscedastic assumption and compares this rule with that of the Bayes rule from the mixture model method. We show that the classification rules of both methods are linear, but the slopes of the two classification lines change in the opposite direction as the component distributions become more elongated. The classification performance of the k-means algorithm is then compared to that of the mixture model method via simulation. The comparison, which is limited to two clusters, shows that the k-means algorithm provides poor classification performances consistently as the component distributions become more elongated while the mixture model method can potentially, but not necessarily, take advantage of this change and provide a much better classification performance. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Clustering Data mining Mixture model k-means algorithm EM algorithm Elongation Mixing proportion Misclassification rate

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Application of the k-means algorithm and the Gaussian Mixture Model (GMM) - African Vulture Optimization algorithm (AVOA) for the Resolution of Overlapping Energy-Dispersive X-Ray Fluorescence (EDXRF) Peaks

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ANALYTICAL LETTERS 2024年第16期57卷 2629-2643页

作者： Niu, Chuanle Li, Fang Ren, Shun Lu, Anxiang Beijing Acad Agr & Forestry Sci Inst Qual Stand & Testing Technol Beijing Peoples R China China Three Gorges Univ Coll Comp & Informat Technol Yichang Peoples R China Beijing Acad Agr & Forestry Sci Inst Qual Stand & Testing Technol Beijing 100097 Peoples R China

Limitations in instrument resolution mean that soil samples analysis using energy-dispersive X-ray fluorescence (EDXRF) often produced overlapping spectral peaks of heavy metals with similar energies. This overlapping of spectral peaks posed a significant problem in the quantitative analysis of heavy metals in soil samples. Therefore, it was necessary to decompose the overlapping peaks. In this paper, a new method for decomposing overlapping peaks was presented in the context of Gaussian Mixture Model (GMM) parameter estimation, combining the k-means algorithm and the African Vulture Optimization algorithm (AVOA). The data acquired from EDXRF instruments were preprocessed before being input to the k-means algorithm, which produces the output used to initialize the parameters of the GMM. Subsequently, AVOA was employed to optimize the GMM parameters based on a fitness function derived from the data. Ultimately, the optimized GMM parameters obtained through the AVOA process yield the optimal solution for the decomposition of overlapping peaks. This approach effectively addressed the challenge of peak overlap in the determination of heavy metals in soil by EDXRF. Soil samples were obtained from national standard samples, peach gardens, and vegetable-growing regions. The results showed that the maximum absolute errors for the peak channel and peak area were 1.46% and 6.37%, respectively. A comparison of the proposed model with others models indicated that the k-means-GMM-AVOA model exhibits the best performance in terms of speed, accuracy, and stability.

关键词： African vulture optimization algorithm (AVOA) energy-dispersive X-ray fluorescence (EDXRF) Gaussian mixture model (GMM) k-means algorithm

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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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A clustering method combining differential evolution with the k-means algorithm

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PATTERN RECOGNITION LETTERS 2011年第12期32卷 1613-1621页

作者： kwedlo, Wojciech Bialystok Tech Univ Fac Comp Sci PL-15351 Bialystok Poland

The present paper considers the problem of partitioning a dataset into a known number of clusters using the sum of squared errors criterion (SSE). A new clustering method, called DE-kM, which combines differential evolution algorithm (DE) with the well known k-means procedure is described. In the method, the k-means algorithm is used to fine-tune each candidate solution obtained by mutation and crossover operators of DE. Additionally, a reordering procedure which allows the evolutionary algorithm to tackle the redundant representation problem is proposed. The performance of the DE-kM clustering method is compared to the performance of differential evolution, global k-means method, genetic k-means algorithm and two variants of the k-means algorithm. The experimental results show that if the number of clusters k is sufficiently large, DE-kM obtains solutions with lower SSE values than the other five algorithms. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Cluster analysis Differential evolution k-means algorithm

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Modeling of network communication instability based on k-means algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第2期39卷 1649-1658页

作者： Hu, Haiyan Su, Chang Jilin Business & Technol Coll Dept Engn Changchun 130507 Peoples R China Changchun Eleventh High Sch Informat Ctr Changchun Peoples R China

In order to overcome the problems of invulnerability and low communication efficiency when analyzing network communication instability with current methods, this paper proposes a modeling method of network communication instability based on k-means algorithm. The network element nodes are generated by clustering idea, and the initial communication topology is constructed. k-means algorithm is used to optimize the initial communication model, build a comprehensive mathematical model of network communication, and solve the model to realize the optimization of communication model. The network efficiency function is used to further quantify the network invulnerability, and the function is used to find the most vulnerable nodes in the network, and strengthen them to achieve efficient control of network invulnerability. The experimental results show that the model has strong invulnerability, up to 99.9%, high communication efficiency and coverage, and the maximum communication delay is only 0.35 s. It is a feasible network communication model.

关键词： k-means algorithm network communication Instability modeling

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