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。-k-means algorithm process as follows: First of all, the object data from the n choose k object as initial cluster centers and the remaining for the other object, then according to their cluster center with those of the similarity (distance) respectively assigned to them with the most similar (represented by cluster center) clustering obtained and then calculated for each new cluster center clustering (all objects in the cluster mean) repeated this process until the standard measure of function until the beginning of convergence. Platform: |
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Author:lining |
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Description: 基于k均值、c均值等聚类算法，应用于数据挖掘-Based on the mean k, c means clustering algorithm, etc., used in data mining Platform: |
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Author:闫少华 |
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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。-First, a data object from the n choose k objects as initial cluster centers and for the rest of the other objects, according to their similarity with the cluster center (distance), respectively, assign them to their most similar (represented by cluster center) clustering then calculated for each cluster center received a new clustering (all objects in the cluster mean) repeats this process until the convergence criteria begin until the measure function. Standard deviation is generally used as a standard measure function. K a cluster has the following characteristics: the cluster itself as a compact, but separated as much as possible between each cluster. Platform: |
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Author:lx |
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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 accept parameter k Then will the n of prior input data object is divided into k clustering to make won clustering meet: the same clustering the object in the similarity is higher And different clustering the object in the smaller similarity. Clustering similarity is using the cluster of the object in the mean gained a "center object" (the center of gravity) to calculate.
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Author:彭立军 |
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Description: PAM（Partitioning Around Medoid,围绕中心点的划分）算法是是划分算法中一种很重要的算法，有时也称为k-中心点算法，是指用中心点来代表一个簇。PAM算法最早由Kaufman和Rousseevw提出，Medoid的意思就是位于中心位置的对象。PAM算法的目的是对n个数据对象给出k个划分。PAM算法的基本思想：PAM算法的目的是对成员集合D中的N个数据对象给出k个划分，形成k个簇，在每个簇中随机选取1个成员设置为中心点，然后在每一步中，对输入数据集中目前还不是中心点的成员根据其与中心点的相异度或者距离进行逐个比较，看是否可能成为中心点。用簇中的非中心点到簇的中心点的所有距离之和来度量聚类效果，其中成员总是被分配到离自身最近的簇中，以此来提高聚类的质量。-PAM (Partitioning Around Medoid Around the division of the center,) algorithm is a kind of partition algorithm is very important algorithm, and sometimes also called k-center algorithm, it is to point to in the center to represent a cluster. The earliest PAM algorithm by Kaufman and Rousseevw puts forward, Medoid mean is at the center of the location of the object. PAM algorithm for the purpose of n data object is given k division. PAM algorithm to the basic idea of the: PAM algorithm for the purpose of members set D is the N data object given k division, forming k cluster, each cluster in selected at random from a members set to center, then at each step, the focus of the input data is not a member of the center according to the center YiDu or phase with each distance is, look to whether can be centered. Use cluster in the center point to the center of the cluster of the sum of all the distance to measure the clustering effect, which is always assigned members from their recent cluste Platform: |
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Author:赵元 |
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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 Platform: |
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Author:liyu |
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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. Platform: |
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Author:lc |
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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. Platform: |
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Author:xiaojade |
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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. Platform: |
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Author:cooldra |
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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. Platform: |
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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. Platform: |
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Author:周雨奇 |
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Description: The number of states in GMM as the generative model of the frames is obtained using
k-means algorithm. This also helps to initialize the mean vector and the covariance
matrix of the individual state of the GMM. The training LPC frames collected from
three speech segments are subjected to PCA for dimensionality reduction and are
subjected to k-means algorithm. The total number of frames is equal to the total
number of vectors that are subjected to k-means clustering. Platform: |
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Author:Khan17
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Description: k均值聚类 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。(kmeans cluster K means clustering algorithm accepts parameters K; N data object classification and the previously input for K clustering in order to make the cluster available to meet the same object: high similarity clustering; and different clustering objects in lower similarity. The clustering similarity is calculated by using a central object (gravity center) obtained from the mean value of objects in each cluster.) Platform: |
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Author:看看联合是啥
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Description: K-Means是聚类算法中的一种，其中K表示类别数，Means表示均值。顾名思义K-Means是一种通过均值对数据点进行聚类的算法。K-Means算法通过预先设定的K值及每个类别的初始质心对相似的数据点进行划分。并通过划分后的均值迭代优化获得最优的聚类结果。(K-Means is one of the clustering algorithms, in which K represents the number of classes, and Means means the mean. As the name suggests, K-Means is an algorithm for clustering data points by means of mean. K-Means algorithm divides the similar data points by the preset K value and the initial centroid of each category. The optimal clustering results are obtained by means of the mean iterative optimization after division.) Platform: |
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Author:DragonFZJ
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Description: 采用PCA白化和K均值对轴承故障进行聚类分析(Clustering analysis of bearing failure by PCA whitening and K mean) Platform: |
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Author:lizeda
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Description: AP聚类算法是基于数据点间的"信息传递"的一种聚类算法。与k-均值算法或k中心点算法不同，AP算法不需要在运行算法之前确定聚类的个数。(AP clustering algorithm is a kind of clustering algorithm based on "information transfer" between data points. Unlike the k- mean algorithm or the k center point algorithm, the AP algorithm does not need to determine the number of clustering before the operation algorithm.) Platform: |
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Author:WanderKing
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Description: 文件夹中主要有二维的K-means，gmm，mean-shift，三维的K-means聚类算法的程序，同时已经经过本人验证无误，可以成功运行，有问题的可以私下交流。(Folder mainly two-dimensional k-means, GMM, mean-shift, three-dimensional k-means clustering algorithm procedures, at the same time has been verified by myself, can be successfully run, there are problems can be private communication.) Platform: |
Size: 10240 |
Author:xuebiao |
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Search - k-mean clustering