Comprehensive K-Means Clustering

The k-means algorithm is a popular data clustering technique due to its speed and simplicity. However, it is susceptible to issues such as sensitivity to the chosen seeds, and inaccurate clusters due to poor initial seeds, particularly in complex datasets or datasets with non-spherical clusters. In this paper, a Comprehensive K-Means Clustering algorithm is presented, in which multiple trials of k-means are performed on a given dataset. The clustering results from each trial are transformed into a five-dimensional data point, containing the scope values of the x and y coordinates of the clusters along with the number of points within that cluster. A graph is then generated displaying the configuration of these points using Principal Component Analysis (PCA), from which we can observe and determine the common clustering patterns in the dataset. The robustness and strength of these patterns are then examined by observing the variance of the results of each trial, wherein a different subset of the data keeping a certain percentage of original data points is clustered. By aggregating information from multiple trials, we can distinguish clusters that consistently emerge across different runs from those that are more sensitive or unlikely, hence deriving more reliable conclusions about the underlying structure of complex datasets. Our experiments show that our algorithm is able to find the most common associations between different dimensions of data over multiple trials, often more accurately than other algorithms, as well as measure stability of these clusters, an ability that other k-means algorithms lack.

Clustering Countries on COVID-19 Data among Different Waves Using K-Means Clustering

The COVID-19 pandemic has caused an unprecedented spike in confirmed cases in 230 countries globally. In this work, a set of data from the COVID-19 coronavirus outbreak has been subjected to two well-known unsupervised learning techniques: K-means clustering and correlation. The COVID-19 virus has infected several nations, and K-means automatically looks for undiscovered clusters of those infections. To examine the spread of COVID-19 before a vaccine becomes widely available, this work has used unsupervised approaches to identify the crucial county-level confirmed cases, death cases, recover cases, total_cases_per_million, and total_deaths_per_million aspects of county-level variables. We combined countries into significant clusters using this feature subspace to assist more in-depth disease analysis efforts. As a result, we used a clustering technique to examine various trends in COVID-19 incidence and mortality across nations. This technique took the key components of a trajectory and incorporates them into a K-means clustering process. We separated the trend lines into measures that characterize various features of a trend. The measurements were first reduced in dimension, then clustered using a K-means algorithm. This method was used to individually calculate the incidence and death rates and then compare them.

光伏波动平抑下改进K-means的电池储能动态分组控制策略

针对电池储能系统(battery energy storage system,BESS)进行光伏波动平抑时寿命损耗高及荷电状态(state of charge,SOC)一致性差的问题,提出了光伏波动平抑下改进K-means的BESS动态分组控制策略。首先,采用最小最大调度方法获取光伏并网指令。其次,设计了改进侏儒猫鼬优化算法(improved dwarf mongoose optimizer,IDMO),并利用它对传统K-means聚类算法进行改进,加快了聚类速度。接着,制定了电池单元动态分组原则,并根据电池单元SOC利用改进K-means将其分为3个电池组。然后,设计了基于充放电函数的电池单元SOC一致性功率分配方法,并据此提出BESS双层功率分配策略,上层确定电池组充放电顺序及指令,下层计算电池单元充放电指令。对所提策略进行仿真验证,结果表明,所设计的IDMO具有更高的寻优精度及更快的寻优速度。所提BESS平抑光伏波动策略在有效平抑波动的同时,降低了BESS运行寿命损耗并提高了电池单元SOC的均衡性。

基于K-means聚类和特征空间增强的噪声标签深度学习算法

深度学习中神经网络的性能依赖于高质量的样本,然而噪声标签会降低网络的分类准确率。为降低噪声标签对网络性能的影响,噪声标签学习算法被提出。该算法首先将训练样本集划分成干净样本集和噪声样本集,然后使用半监督学习算法对噪声样本集赋予伪标签。然而,错误的伪标签以及训练样本数量不足的问题仍然限制着噪声标签学习算法性能的提升。为解决上述问题,提出基于K-means聚类和特征空间增强的噪声标签深度学习算法。首先,该算法利用K-means聚类算法对干净样本集进行标签聚类,并根据噪声样本集与聚类中心的距离大小筛选出难以分类的噪声样本,以提高训练样本的质量;其次,使用mixup算法扩充干净样本集和噪声样本集,以增加训练样本的数量;最后,采用特征空间增强算法抑制mixup算法新生成的噪声样本,从而提高网络的分类准确率。并在CIFAR10、CIFAR100、MNIST和ANIMAL-10共4个数据集上试验验证了该算法的有效性。

基于蚁群算法的三支k-means聚类算法

在聚类分析中,三支k-means聚类算法较具有较强的处理边界不确定数据的能力,但仍然存在对初始聚类中心敏感的问题.通过将蚁群算法和三支k-means聚类算法相结合,给出了一种基于蚁群算法的三支k-means聚类算法来解决这一问题.利用蚁群算法中随机概率选择策略和信息素的正负反馈机制,动态调整权重的方法,对三支k-means聚类算法进行优化.在UCI数据集上实验证明,该方法对聚类结果的性能指标有所提高.

启发式k-means聚类算法的改进研究

启发式k-means聚类算法通过在k-means第一次迭代后查看附近的集群来预测每个数据点可能会被划分到的集群子集,有效地加快了算法的运行速度。但由于启发式算法存在随机选择初始聚类中心以及无法有效识别数据集中离群点的缺陷,导致聚类结果的误差平方和较大并且轮廓系数偏小。针对这一问题,提出了CHk-means算法,该算法引入仔细播种方法,克服了启发式k-means算法随机选择初始聚类中心带来的局部最优解问题;该算法引入局部异常因子LOF算法对离群点进行检测,降低了离群点数据对聚类结果的影响。在多个数据集上对3种算法进行对比试验,结果表明CHk-means算法可有效降低聚类结果的误差平方和,增强聚类的轮廓系数,使聚类质量得到明显改善。

基于K-means聚类和BP神经网络的电梯能耗实时监测方法

针对现有方法在对电梯能耗进行监测时,存在监测精度低、用时长、监测结果不理想的问题,该文提出一种基于K-means聚类算法和BP神经网络相结合的电梯能耗实时监测方法 .在经过清洗的能耗数据中提取影响建筑能耗实时监测的主要因素特征值,利用相似系数法进行相似度计算,获取相似系数.对相似电梯能耗数据进行小波分解获取高低频序列,分别采用LSSVM-GSA检测方法和均方加权处理方法对低频和高频部分进行处理,将两个结果进行重构,得到最终的实时监测结果 .仿真实验结果表明:所提方法能够获取高精度、低耗时、高稳定性的监测结果 .

一种融合乌鸦搜索算法的K-means聚类算法

传统的K-均值聚类算法(K-means)对初始聚类中心的选择敏感,容易陷入局部最优解,并且需要预先设定聚类数量K,这在实际操作中往往难以实现。为了解决这些问题,提出了一种融合乌鸦搜索算法的K-means聚类算法。该算法利用乌鸦搜索算法的全局搜索能力,自动确定最佳的聚类数目K,从而提高聚类的质量和效率。通过在Seeds数据集进行实验计算卡林斯基-哈拉巴斯(Calinski-Harabasz)指数等评价指标,发现该算法聚类效果明显优于传统的K-means算法。

Watershed classification by remote sensing indices： A fuzzy c-means clustering approach

Determining the relatively similar hydrological properties of the watersheds is very crucial in order to readily classify them for management practices such as flood and soil erosion control. This study aimed to identify homogeneous hydrological watersheds using remote sensing data in western Iran. To achieve this goal, remote sensing indices including SAVI, LAI, NDMI, NDVI and snow cover, were extracted from MODIS data over the period 2000 to 2015. Then, a fuzzy method was used to clustering the watersheds based on the extracted indices. A fuzzy c-mean(FCM) algorithm enabled to classify 38 watersheds in three homogeneous groups.The optimal number of clusters was determined through evaluation of partition coefficient, partition entropy function and trial and error. The results indicated three homogeneous regions identified by the fuzzy c-mean clustering and remote sensing product which are consistent with the variations of topography and climate of the study area. Inherently,the grouped watersheds have similar hydrological properties and are likely to need similar management considerations and measures.

基于改进K-means算法的物流配送中心选址研究

针对传统K-means算法需要主观设定K值及无法处理类别型数据问题,文章运用肘部法及轮廓系数法确定合理K值,对类别型数据采取独热编码(One-Hot Encoding)转换为可以处理的连续型数据,并将其运用到在物流配送中心选址中;并综合考虑多种类别的影响因素,构建了相应的影响因素指标体系,提出的模型能够识别输入数据的数值型及类别型数据,实现样本的有效聚类。相关的案例分析结果表明,相比传统K-means聚类,文章的改进K-means算法选址结果可使物流总成本降低8.76%,运营成本降低14.85%,固定成本降低8.09%,效果显著。

A Fixed Suppressed Rate Selection Method for Suppressed Fuzzy C-Means Clustering Algorithm

Suppressed fuzzy c-means (S-FCM) clustering algorithm with the intention of combining the higher speed of hard c-means clustering algorithm and the better classification performance of fuzzy c-means clustering algorithm had been studied by many researchers and applied in many fields. In the algorithm, how to select the suppressed rate is a key step. In this paper, we give a method to select the fixed suppressed rate by the structure of the data itself. The experimental results show that the proposed method is a suitable way to select the suppressed rate in suppressed fuzzy c-means clustering algorithm.

A New Method of Wind Turbine Bearing Fault Diagnosis Based on Multi-Masking Empirical Mode Decomposition and Fuzzy C-Means Clustering

Based on Multi-Masking Empirical Mode Decomposition (MMEMD) and fuzzy c-means (FCM) clustering, a new method of wind turbine bearing fault diagnosis FCM-MMEMD is proposed, which can determine the fault accurately and timely. First, FCM clustering is employed to classify the data into different clusters, which helps to estimate whether there is a fault and how many fault types there are. If fault signals exist, the fault vibration signals are then demodulated and decomposed into different frequency bands by MMEMD in order to be analyzed further. In order to overcome the mode mixing defect of empirical mode decomposition (EMD), a novel method called MMEMD is proposed. It is an improvement to masking empirical mode decomposition (MEMD). By adding multi-masking signals to the signals to be decomposed in different levels, it can restrain low-frequency components from mixing in highfrequency components effectively in the sifting process and then suppress the mode mixing. It has the advantages of easy implementation and strong ability of suppressing modal mixing. The fault type is determined by Hilbert envelope finally. The results of simulation signal decomposition showed the high performance of MMEMD. Experiments of bearing fault diagnosis in wind turbine bearing fault diagnosis proved the validity and high accuracy of the new method.

New two-dimensional fuzzy C-means clustering algorithm for image segmentation

To solve the problem of poor anti-noise performance of the traditional fuzzy C-means (FCM) algorithm in image segmentation, a novel two-dimensional FCM clustering algorithm for image segmentation was proposed. In this method, the image segmentation was converted into an optimization problem. The fitness function containing neighbor information was set up based on the gray information and the neighbor relations between the pixels described by the improved two-dimensional histogram. By making use of the global searching ability of the predator-prey particle swarm optimization, the optimal cluster center could be obtained by iterative optimization, and the image segmentation could be accomplished. The simulation results show that the segmentation accuracy ratio of the proposed method is above 99%. The proposed algorithm has strong anti-noise capability, high clustering accuracy and good segment effect, indicating that it is an effective algorithm for image segmentation.

Development of slope mass rating system using K-means and fuzzy c-means clustering algorithms

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.

AN IMPROVED ALGORITHM FOR SUPERVISED FUZZY C-MEANS CLUSTERING OF REMOTELY SENSED DATA

This paper describes an improved algorithm for fuzzy c-means clustering of remotely sensed data, by which the degree of fuzziness of the resultant classification is de- creased as comparing with that by a conventional algorithm: that is, the classification accura- cy is increased. This is achieved by incorporating covariance matrices at the level of individual classes rather than assuming a global one. Empirical results from a fuzzy classification of an Edinburgh suburban land cover confirmed the improved performance of the new algorithm for fuzzy c-means clustering, in particular when fuzziness is also accommodated in the assumed reference data.

基于改进K-means聚类和皮尔逊相关系数户变关系异常诊断

用电信息采集系统易出现台区户变关系错误问题,传统诊断技术主要针对少用户台区出现异常用户情况,但对于多达数百用户台区,存在多相邻台区异常用户特征提取难题。文中首先通过主成分分析对GIS系统获取台区总表和用户电表电压数据实现降维,建立改进K-means聚类提取电压数据特征,提出改进皮尔逊相关系数算法分析待检测用户,据此建立基于改进K-means聚类和改进皮尔逊相关系数的户变关系异常诊断方法,实现多异常用户所属正确台区诊断。实际算例分析结果表明,文中提出算法在识别同一台区一个及多个异常用户、不同台区多个异常用户情况下均能有效实现异常用户的准确检测与分析,相比传统检测方法,实现简单且准确性更高。

基于BBO优化K-means算法的WSN分簇路由算法

针对无线传感器网络中传感器节点能量有限、网络生存期短的问题,提出一种基于生物地理学算法优化K-means的无线传感器网络分簇路由算法BBOK-GA。成簇阶段,通过生物地理学优化算法改进K-means算法,避免求解时陷入局部最优。根据能量因子和距离因子设计了新的适应度函数选举最优簇首,完成分簇任务。数据传输阶段,则利用遗传算法为簇首节点搜寻到基站的最佳数据传输路径。仿真结果表明,相较于LEACH、LEACH-C、K-GA等算法,BBOK-GA降低了网络能耗,提高了网络吞吐量,延长了网络生存周期。

一种基于K-means聚类算法的沙尘天气客观识别方法

鉴于以往基于污染物浓度时间序列进行分析的沙尘天气识别方法在判断标准上存在一定的主观性,本文提出一种基于K-means聚类算法的沙尘天气客观识别方法。本方法利用环境监测总站的PM2.5和PM10小时浓度资料进行聚类,首先选取最优的分类数目K进行聚类,其次对聚类结果中离散程度较高的类别进行再次聚类,直到无需分类。将本方法应用于西安市2018年2~4月沙尘天气的识别中,结果表明,本方法可有效识别主要沙尘天气。此外,利用本方法可得到沙尘天气典型特征:PM2.5占PM10浓度的比例小于43.5%、PM10浓度高于228μg/m^(3,)符合沙尘天气期间PM10浓度较高且以粗颗粒物为主的物理特征。总体上看,本方法物理基础清晰,可操行性强,适用于大规模数据处理,具有较好的实用价值和应用前景。

融合异常检测与区域分割的高效K-means聚类算法

传统K-means及其众多改进算法缺乏显式处理异常样本的能力,导致其聚类性能容易受到异常样本的影响。针对此问题,提出一种融合异常检测与区域分割的高效K-means聚类算法。首先,通过构建统一聚类模型,形成异常检测与聚类之间的交互协同,以提高聚类性能。其次,利用近邻簇搜索技术对各类簇进行自适应的区域分割,以减少冗余计算,提高算法执行效率。最后,为验证所提方法的有效性,在多个合成数据集和真实数据集上分别进行测试。实验结果表明:所提算法聚类性能和执行效率优于其他算法;在添加10%异常样本的Wine数据集上准确度可达0.911。

基于优化K-means算法的高校成绩聚类分析研究

针对经典K均值算法在聚类中心易受异常值影响,导致聚类结果不稳定的问题,提出基于样本分布密度的优化K-means算法,以提高聚类稳定性和准确性;聚类后通过CH指数和分类区间占比总体两种方法,客观评价3种离散化方法,结果表明,优化的K-means算法避免了区间分类不合理现象,更加准确地反映了成绩样本的分布特点。