Coordinate Descent K-means Algorithm Based on Split-Merge

The Coordinate Descent Method for K-means(CDKM)is an improved algorithm of K-means.It identifies better locally optimal solutions than the original K-means algorithm.That is,it achieves solutions that yield smaller objective function values than the K-means algorithm.However,CDKM is sensitive to initialization,which makes the K-means objective function values not small enough.Since selecting suitable initial centers is not always possible,this paper proposes a novel algorithm by modifying the process of CDKM.The proposed algorithm first obtains the partition matrix by CDKM and then optimizes the partition matrix by designing the split-merge criterion to reduce the objective function value further.The split-merge criterion can minimize the objective function value as much as possible while ensuring that the number of clusters remains unchanged.The algorithm avoids the distance calculation in the traditional K-means algorithm because all the operations are completed only using the partition matrix.Experiments on ten UCI datasets show that the solution accuracy of the proposed algorithm,measured by the E value,is improved by 11.29%compared with CDKM and retains its efficiency advantage for the high dimensional datasets.The proposed algorithm can find a better locally optimal solution in comparison to other tested K-means improved algorithms in less run time.

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

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.

A Hybrid Method Combining Improved K-means Algorithm with BADA Model for Generating Nominal Flight Profiles

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.

Similarity matrix-based K-means algorithm for text clustering

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.

An Improved K-Means Algorithm Based on Initial Clustering Center Optimization

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.

Polarimetric Meteorological Satellite Data Processing Software Classification Based on Principal Component Analysis and Improved K-Means Algorithm

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.

A Nonuniform Clustering Routing Algorithm Based on an Improved K-Means Algorithm

In a large-scale wireless sensor network(WSN),densely distributed sensor nodes process a large amount of data.The aggregation of data in a network can consume a great amount of energy.To balance and reduce the energy consumption of nodes in a WSN and extend the network life,this paper proposes a nonuniform clustering routing algorithm based on the improved K-means algorithm.The algorithm uses a clustering method to form and optimize clusters,and it selects appropriate cluster heads to balance network energy consumption and extend the life cycle of the WSN.To ensure that the cluster head(CH)selection in the network is fair and that the location of the selected CH is not concentrated within a certain range,we chose the appropriate CH competition radius.Simulation results show that,compared with LEACH,LEACH-C,and the DEEC clustering algorithm,this algorithm can effectively balance the energy consumption of the CH and extend the network life.

SMK-means:An Improved Mini Batch K-means Algorithm Based on Mapreduce with Big Data

In recent years,the rapid development of big data technology has also been favored by more and more scholars.Massive data storage and calculation problems have also been solved.At the same time,outlier detection problems in mass data have also come along with it.Therefore,more research work has been devoted to the problem of outlier detection in big data.However,the existing available methods have high computation time,the improved algorithm of outlier detection is presented,which has higher performance to detect outlier.In this paper,an improved algorithm is proposed.The SMK-means is a fusion algorithm which is achieved by Mini Batch K-means based on simulated annealing algorithm for anomalous detection of massive household electricity data,which can give the number of clusters and reduce the number of iterations and improve the accuracy of clustering.In this paper,several experiments are performed to compare and analyze multiple performances of the algorithm.Through analysis,we know that the proposed algorithm is superior to the existing algorithms.

Dynamic grouping control of electric vehicles based on improved k-means algorithm for wind power fluctuations suppression

To address the significant lifecycle degradation and inadequate state of charge(SOC)balance of electric vehicles(EVs)when mitigating wind power fluctuations,a dynamic grouping control strategy is proposed for EVs based on an improved k-means algorithm.First,a swing door trending(SDT)algorithm based on compression result feedback was designed to extract the feature data points of wind power.The gating coefficient of the SDT was adjusted based on the compression ratio and deviation,enabling the acquisition of grid-connected wind power signals through linear interpolation.Second,a novel algorithm called IDOA-KM is proposed,which utilizes the Improved Dingo Optimization Algorithm(IDOA)to optimize the clustering centers of the k-means algorithm,aiming to address its dependence and sensitivity on the initial centers.The EVs were categorized into priority charging,standby,and priority discharging groups using the IDOA-KM.Finally,an two-layer power distribution scheme for EVs was devised.The upper layer determines the charging/discharging sequences of the three EV groups and their corresponding power signals.The lower layer allocates power signals to each EV based on the maximum charging/discharging power or SOC equalization principles.The simulation results demonstrate the effectiveness of the proposed control strategy in accurately tracking grid power signals,smoothing wind power fluctuations,mitigating EV degradation,and enhancing the SOC balance.

Genetic Algorithm Combined with the K-Means Algorithm:A Hybrid Technique for Unsupervised Feature Selection

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.

Multiple Parameter Based Clustering (MPC): Prospective Analysis for Effective Clustering in Wireless Sensor Network (WSN) Using K-Means Algorithm

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.

Enhanced KOCED Routing Protocol with K-means Algorithm

Replacing or recharging batteries in the sensor nodes of a wireless sensor network(WSN)is a significant challenge.Therefore,efficient power utilization by sensors is a critical requirement,and it is closely related to the life span of the network.Once a sensor node consumes all its energy,it will no longer function properly.Therefore,various protocols have been proposed to minimize the energy consumption of sensors and thus prolong the network operation.Recently,clustering algorithms combined with artificial intelligence have been proposed for this purpose.In particular,various protocols employ the K-means clustering algorithm,which is a machine learning method.The number of clustering configurations required by the K-means clustering algorithm is greater than that required by the hierarchical algorithm.Further,the selection of the cluster heads considers only the residual energy of the nodes without accounting for the transmission distance to the base station.In terms of energy consumption,the residual energy of each node,the transmission distance,the cluster head location,and the central relative position within the cluster should be considered simultaneously.In this paper,we propose the KOCED(K-means with Optimal clustering for WSN considering Centrality,Energy,and Distance)protocol,which considers the residual energy of nodes as well as the distances to the central point of the cluster and the base station.A performance comparison shows that the KOCED protocol outperforms the LEACH protocol by 259%(223 rounds)for first node dead(FND)and 164%(280 rounds)with 80%alive nodes.

Improvement of energy resolution of x-ray transition-edge sensor using K-means algorithm and Wiener filter

We develop an x-ray Ti/Au transition-edge sensor(TES)with an Au absorber deposited on the center of TES and improved its energy resolution using the K-means clustering algorithm in combination with Wiener filter.We firstly extract the main parameters of each recorded pulse trace,which are adopted to classify these traces into several clusters in the K-means clustering algorithm.Then real traces are selected for energy resolution analysis.Following the baseline correction,the Wiener filter is used to improve the signal-to-noise ratio.Although the silicon underneath the TES has not been etched to reduce the thermal conductance,the energy resolution of the developed x-ray TES is improved from 94 eV to 44 eV at 5.9 keV.

Parallel K-Means Algorithm for Shared Memory Multiprocessors

Clustering is the task of assigning a set of instances into groups in such a way that is dissimilarity of instances within each group is minimized. Clustering is widely used in several areas such as data mining, pattern recognition, machine learning, image processing, computer vision and etc. K-means is a popular clustering algorithm which partitions instances into a fixed number clusters in an iterative fashion. Although k-means is considered to be a poor clustering algorithm in terms of result quality, due to its simplicity, speed on practical applications, and iterative nature it is selected as one of the top 10 algorithms in data mining [1]. Parallelization of k-means is also studied during the last 2 decades. Most of these work concentrate on shared-nothing architectures. With the advent of current technological advances on GPU technology, implementation of the k-means algorithm on shared memory architectures recently start to attract some attention. However, to the best of our knowledge, no in-depth analysis on the performance of k-means on shared memory multiprocessors is done in the literature. In this work, our aim is to fill this gap by providing theoretical analysis on the performance of k-means algorithm and presenting extensive tests on a shared memory architecture.

Study on the Application of K-Means Algorithm Implemented Hadoop Platform to the Library Work in Colleges and Universities

In this paper, the borrowing data of readers is analyzed and studied by taking K-Means algorithm as an example and implementing this algorithm in Hadoop calculation platform, and data mining technology is effectively and closely combined with personalized library service through the experimental data.

基于改进K-means算法的室内可见光通信O-OFDM系统信道均衡技术

在室内可见光通信中符号间干扰和噪声会严重影响系统性能,K均值(K-means)均衡方法可以抑制光无线信道的影响,但其复杂度较高,且在聚类边界处易出现误判。提出了改进聚类中心点的K-means(Improved Center K-means,IC-Kmeans)算法,通过随机生成足够长的训练序列,然后将训练序列每一簇的均值作为K-means聚类中心,避免了传统K-means反复迭代寻找聚类中心。进一步,提出了基于神经网络的IC-Kmeans(Neural Network Based IC-Kmeans,NNIC-Kmeans)算法,使用反向传播神经网络将接收端二维数据映射至三维空间,以增加不同簇之间混合数据的距离,提高了分类准确性。蒙特卡罗误码率仿真表明,IC-Kmeans均衡和传统K-means算法的误码率性能相当,但可以显著降低复杂度,特别是在信噪比较小时。同时,在室内多径信道模型下,与IC-Kmeans和传统Kmeans均衡相比,NNIC-Kmeans均衡的光正交频分复用系统误码率性能最好。

基于K-means/RPF的大型遮蔽空间人员定位算法

针对大型遮蔽空间惯性/地图匹配算法中粒子贫化和子粒子群迷路效应导致定位精度下降的问题,提出一种基于K-means聚类的回溯粒子滤波(RPF)人员定位算法。首先,用行人航位推算(PDR)中航向更新、步频检测及步长估计得到初始运动轨迹;然后,设计RPF算法,提高存活粒子有效性和多样性,缓解粒子贫化,提高人员定位精度;最后,通过K-means聚类算法解决子粒子群的迷路效应,修正人员轨迹出现在非可行域的现象。实验结果表明:本文算法抑制了粒子贫化和子粒子群迷路效应,人员平均定位误差相比惯性定位和标准粒子滤波降低了81.20%和51.48%。

基于k-means聚类的无线传感器网络低功耗路由算法

为提高无线传感器网络(WSN)中传感器节点的能量利用率以延长传感器网络的生命周期,提出基于k-means聚类的WSN低功耗路由算法。先按照距离乘积最大规则选取聚类初始簇中心,并在k-means算法迭代过程中引入能耗因子来优化k-means的分簇效果,降低基站附近节点的能耗和簇内的数据传输能耗;再使用Dijkstra算法搜寻簇首和基站间的最低功耗传输路径,以降低簇首能耗。仿真结果表明,该算法提高了网络的能量利用率,有效延长了网络的生命周期,使首个死亡节点延后出现,对WSN实现了更好的优化。

Improved k-means clustering algorithm

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.