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Description: 输入：聚类个数k，以及包含 n个数据对象的数据库。输出：满足方差最小标准的k个聚类。处理流程： （1）从 n个数据对象任意选择 k 个对象作为初始聚类中心.
（2）根据每个聚类对象的均值（中心对象），计算每个对象与这些中心对象的距离；并根据最小距离重新对相应对象进行划分；（3）重新计算每个（有变化）聚类的均值（中心对象）
（4）循环（2）到（3）直到每个聚类不再发生变化为止-Input: number of clusters k, and n data object contains a database. Output: meet the standard minimum variance k-clustering. Processes: (1) n data objects from arbitrarily selected k object as initial cluster centers. (2) based on the mean of each cluster object (central object), calculated for each object and the distance to the object of these centers and according to the minimum distance to re-divide the corresponding object (3) re-calculated for each (a change) clustering means (central object) (4) Cycle (2) to (3) until no further change in each cluster until the
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Author: liyu |
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Description: This paper presents a new cluster validity index for nding a suitable number of
fuzzy clusters with crisp and fuzzy data. The new index, called the ECAS-index, contains
exponential compactness and separation measures. These measures indicate homogeneity
within clusters and heterogeneity between clusters, respectively. Moreover, a fuzzy c-mean
algorithm is used for fuzzy clustering with crisp data, and a fuzzy k-numbers clustering is
used for clustering with fuzzy data. In comparison to other indices, it is evident that the proposed index is more e ffective and robust under
di fferent conditions of data sets, such as noisy environments and large data sets.
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Author: m |
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Description: 使用Java实现K-means（C均值）聚类算法-Using Java to achieve K-means (C mean) clustering algorithm
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Author: dy |
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Description: 系统辨识作业 考虑如下模型
z(k)-1.5z(k-1)+0.7z(k-2)=u(k-1)+0.5u(k-2)+v(k)
其中v(k)是均值为0，方差为1的白噪声。根据模型生成数据，采用递推最小二乘法对模型参数进行辨识，要求绘出各参数随时间的变化曲线。
-"System identification" computer test 。Consider the following model
Z (k) to 1.5 of z (k- 1)+ 0.7 z (k- 2) = u (k- 1)+ 0.5 u (k- 2)+ v (k)
The v (k) is the mean to 0, the white noise variance 1. Generate data according to the model, the recursive least squares method is adopted to model parameters are identified, draw the parameters changing with time curve.
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Author: liming |
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Description: k均值聚类算法，使各个样本与所在类均值的误差平方和达到最小，并且附有显示程序-k-means clustering algorithm, where the class so that each sample and the mean squared error to a minimum, and with the display program
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Size: 2048 |
Author: 木木 |
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Description: k-means 算法接受参数 k ；然后将事先输入的n个数据对象划分为 k个聚类以便使得所获得的聚类满足：同一聚类中的对象相似度较高；而不同聚类中的对象相似度较小。聚类相似度是利用各聚类中对象的均值所获得一个“中心对象”（引力中心）来进行计算的。-k-means algorithm accepts parameters k n and the previously input data is divided into k-clustering objects in order to make the obtained cluster met: the same high similarity clustering objects objects and different clustering Similarity small. The use of the cluster similarity clustering objects obtained by a mean of " central object" (center of gravity) to be calculated for.
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Author: lc |
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Description: K-means算法，算法步骤如下：
Step1.利用式(2)计算距离矩阵D=()，其中=dist[i, j] ()；
Step2.扫描坐标距离矩阵D，寻找距离的最大值和最小值，用式(3)计算limit；
Step3.扫描坐标距离矩阵D，寻找矩阵中距离最小的2个数据a，b，将数据a，b加入集合，={a,b}，同时将数据a，b从U中删除，更新距离矩阵D；
Step4.利用 (4)式在U中寻找距离集合最近的数据样本t，如果小于limit，则将t加入集合，同时将t从集合U中删除，更新距离矩阵D，重复Step5，否则停止；
Step5.若i<k，i=i+1，重复步骤Step3、Step4，直至k个集合完成；
Step6.取集合中数据的算术平均值记作数据中心，并计算得到的坐标值，完成k个数据中心的选取。-Algorithm steps are as follows:
Step1. Type (2) is used to calculate the distance matrix D = (), including = dist [I, j] ()
Step2. Scan coordinate distance matrix D, looking for the maximum and the minimum distance, use type (3) calculate the limit
Step3. Scan coordinate distance matrix D, looking for matrix minimum distance of two data a, b, and the data to a, b to join the collection, = {a, b}, at the same time the data a, b is removed from the U, update the distance matrix D
Step4. Using (4) in the U find closest to the collection of data samples t, if less than the limit, then t join collection, at the same time t is removed from the set U, update the distance matrix D, repeat Step5, otherwise stop
Step5. If I < k, I = I+ 1, repeat steps Step3, Step4, until k collection is complete
Step6. Take the arithmetic mean of the collection of data for the data center, and to calculate the coordinates, to complete the selection of k data center.
The above steps distribution cu
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Author: ming |
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Description: 用模糊K均值（FCM）的方法对图像进行分类-fuzzy c mean clustering (FCM),segmentation
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Size: 1024 |
Author: 张雷 |
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Description: 当系统的结构参数和负荷情况都已给定时，调节可利用的控制变量(如发电机输出功率、可调变压器抽头等)来找到能满足所有运行约束条件的，并使系统的某一性能指标(如发电成本或网络损耗)达到最优值下的潮流分布。-OPF is a fundamental tool in power system planning and operation
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Author: tsingleo |
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Description: 混合高斯模型使用K（基本为3到5个） 个高斯模型来表征图像中各个像素点的特征,在新一帧图像获得后更新混合高斯模型,用当前图像中的每个像素点与混合高斯模型匹配,如果成功则判定该点为背景点, 否则为前景点。通观整个高斯模型，他主要是有方差和均值两个参数决定，,对均值和方差的学习,采取不同的学习机制,将直接影响到模型的稳定性、精确性和收敛性。由于我们是对运动目标的背景提取建模，因此需要对高斯模型中方差和均值两个参数实时更新。为提高模型的学习能力,改进方法对均值和方差的更新采用不同的学习率 为提高在繁忙的场景下,大而慢的运动目标的检测效果,引入权值均值的概念,建立背景图像并实时更新,然后结合权值、权值均值和背景图像对像素点进行前景和背景的分类。-Gaussian mixture model using K (essentially 3-5) Gaussian model to characterize the features of each pixel in the image, in the image of the new frame for updated Gaussian mixture model, with each pixel in the image with a Gaussian mixture current model matching, if successful, determined that the point of the background points, otherwise the former attraction. Throughout the entire Gaussian model, he mainly has two parameters determine the variance and the mean, the mean and variance of the study, to take a different learning mechanism, will directly affect the stability, accuracy and convergence model. Since we are moving object extraction of the background modeling, so the need for the Gaussian model variance and mean two parameters real-time updates. In order to improve the learning ability of the model, an improved method for updating the mean and variance of different learning rates to improve in the busy scene, large and slow moving object detection results, the introduction of
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Author: 尹安然 |
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Description: image segmentation with k-means algorithm and mean shift clustering and filtering
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Author: chawki |
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Description: 对叶片进行预处理和提取部分特征：1.均值去噪 2.k均值聚类提取复杂背景下的叶子图片 3.填充孔洞 4.去叶柄 5.提取轮廓 6计算纵横轴比、面积凹凸比、周长凹凸比、球形性、圆形性、偏心率、形状参数和矩形度等特征值并进行画图。-
Pretreatment of leaves and extract some of the characteristics: a mean denoising 2.k means clustering leaf extract complex background picture fill holes 3 4 5 contour extraction petiole 6 to calculate the vertical and horizontal axis ratio, convex area ratio.. convex perimeter than spherical, circularity, eccentricity, and rectangular shape parameter values and other characteristics of the drawing.
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Author: 青 |
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Description: k-means 算法的工作过程说明如下：首先从n个数据对象任意选择 k 个对象作为初始聚类中心；而对于所剩下其它对象，则根据它们与这些聚类中心的相似度（距离），分别将它们分配给与其最相似的（聚类中心所代表的）聚类；然后再计算每个所获新聚类的聚类中心（该聚类中所有对象的均值）；不断重复这一过程直到标准测度函数开始收敛为止。一般都采用均方差作为标准测度函数. k个聚类具有以下特点：各聚类本身尽可能的紧凑，而各聚类之间尽可能的分开。下面给出我写的源代码。-work process k-means algorithm is as follows: First, choose k objects from n data objects as the initial cluster centers while for the rest of the other objects, according to the similarity (distance) with those of their cluster centers, They were assigned to the most similar (represented by the cluster center) clustering then calculated for each cluster received new cluster center (the cluster mean all objects) repeats this process Until the beginning of a standard measure function convergence. MSE is generally used as the standard measure function k clustering has the following characteristics: each cluster itself as compact as possible, and to separate between the clusters as possible. Here is what I wrote the source code.
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Author: xiaojade |
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Description: Robert Gordon University的一个研究人员写的k-means聚类算法工具箱，内容完整可运行。-Includes k-means, hierarchical (single-, complete- and mean-linkage), EM for Gaussian Mixture Models, fuzzy c-means, and a demo.
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Author: xavier |
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Description: K-均值聚类算法,基本算法代码，算法的目的是使各个样本与所在类均值的误差平方和达到最小。-The purpose of K-means clustering algorithm, the basic algorithm code, the algorithm is to make the class where each sample mean squared error is minimized.
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Author: angelia |
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Description: 假设用图示所示的两个正交信号经由一个AWGN信道传输二进制信息，在持续期Tb的每个比特区间接收到的信号以10/Tb速率采样，即每个比特区间内10个样本，幅度为A。噪声是均值为零，方差为 的高斯过程。
写MATLAB程序，在方差为0,0.1,1.0和2.0时，完成接收信号和两种发射信号的每一种的离散时间相关，画出在时刻k=1,2,…,10相关器的输出。-Assuming an AWGN channel transmission via binary information in two orthogonal signals icon shown in the ratio of each signal received indirect SAR duration Tb to 10/Tb sampling rate, that is, within the range 10 samples per bit, amplitude A. Noise is zero mean and variance of the Gaussian process.
Write a MATLAB program, the variance is 0,0.1,1.0 and 2.0, to complete each of the two discrete-time signals and transmit the received signal correlation shown in the time k = 1,2, ..., 10 output of the correlator .
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Author: 卢昳丽 |
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Description: k-means 算法接受输入量 k ；然后将n个数据对象划分为 k个聚类以便使得所获得的聚类满足：同一聚类中的对象相似度较高；而不同聚类中的对象相似度较小。聚类相似度是利用各聚类中对象的均值所获得一个“中心对象”（引力中心）来进行计算的。-k-means algorithm accepts input k then n data objects into k clusters in order to make clustering satisfy obtained: the objects in the same cluster high similarity different clustering object similarity small. Cluster similarity is the use of the mean of each cluster of objects that get a " central object" (center of gravity) to perform the calculation.
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Author: cooldra |
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Description: k-means clustering is a method of vector quantization, originally signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.-k-means clustering is a method of vector quantization, originally signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
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Author: mouny |
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Description: k-Means 算法接受输入量 k ；然后将 n 个数据对象划分为 k 个聚类以便使得所获得的聚类满足：同一聚类中的对象相似度较高；而不同聚类中的对象相似度较小。聚类相似度是利用各聚类中对象的均值所获得一个 “ 中心对象 ” （引力中心）来进行计算的。-K-Means algorithm accepts input amount of K then the object n data is divided into k cluster so that the obtained clustering meet: high similarity in the same cluster while in different cluster object similarity is smaller. Cluster similarity is the use of the mean of each cluster obtained by objects in a center (center of gravity) to calculate.
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Author: dragon |
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Description: 首先从n个数据对象任意选择 k 个对象作为初始聚类中心；而对于所剩下其它对象，则根据它们与这些聚类中心的相似度（距离），分别将它们分配给与其最相似的（聚类中心所代表的）聚类；然 后再计算每个所获新聚类的聚类中心（该聚类中所有对象的均值）；不断重复这一过程直到标准测度函数开始收敛为止。一般都采用均方差作为标准测度函数. k个聚类具有以下特点：各聚类本身尽可能的紧凑，而各聚类之间尽可能的分开。
该算法的最大优势在于简洁和快速。算法的关键在于初始中心的选择和距离公式。
-First, choose k objects n data object as initial cluster centers and for the rest of the other objects, according to their similarity (distance) These cluster centers, respectively assign them to its most similar ( cluster centers represent) clustering and then calculate each cluster center obtained new cluster (the cluster mean all objects) repeats this process until the beginning of the standard measurement function converges. Are generally used as the standard deviation measurement function k clusters has the following characteristics: Each cluster itself as compact as possible, but as much as possible to separate between the clusters. The biggest advantage of this algorithm is simple and fast. The key algorithm is the selection and initial center of the distance formula.
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Author: 周雨奇 |
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