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Description: This directory contains code implementing the K-means algorithm. Source codemay be found in KMEANS.CPP. Sample data isfound in KM2.DAT. The KMEANSprogram accepts input consisting of vectors and calculates the givennumber of cluster centers using the K-means algorithm. Output isdirected to the screen.
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Author: 刘思 |
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Description: 由于K－均值聚类算法局部最优的特点，而模拟退火算法理论上具有全局最优的特点。因此，用模拟退火算法对聚类进行了改进。20组聚类仿真表明，平均每次对K结果值改进8次左右，效果显著。下一步工作：实际上在高温区随机生成邻域是个组合爆炸问题（见本人上载软件‘k－均值聚类算法’所述），高温跳出局部解的概率几乎为0，因此正考虑采用凸包约束进行模拟聚类，相关工作正在进行。很快将奉献给各位朋友。-as K-means clustering algorithm for optimal local characteristics, and simulated annealing algorithm theory with the characteristics of the global optimum. Thus, simulated annealing algorithm for clustering improvements. Cluster Group of 20 simulations show that the average value of K results improved about eight times, the results are obvious. The next step : In fact, in high temperature generated random neighborhood is a combination of explosives (see my software on the 'k-means clustering algorithm' mentioned above), high-temperature solution of partial out almost zero probability, it is considering the use of convex hull bound for simulation cluster, the work under way . Soon dedication to the ladies.
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Author: 韩磊 |
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Description: kmeans This directory contains code implementing the K-means algorithm. Source code may be found in KMEANS.CPP. Sample data isfound in KM2.DAT. The KMEANS program accepts input consisting of vectors and calculates the given number of cluster centers using the K-means algorithm. Output is directed to the screen. Usage for KMEANS is: KMEANS SOURCEFILE <enter> The format of the source file is: NPat - Number of patterns (int) SizeVect - Size of vector (int) NClust - Number of cluster centers(int) vect[1,1] ... vect[1,SizeVect] - vector 1 (real) vect[2,1] ... vect[2,SizeVect] - vector 2 (real) . . . . . . vect[NPat,1] ... vect[NClust,SizeVector] - vector N (real) To compile: ICC KMEANS.CPP <enter> -kmeans This directory contains code implementing the K-means algorithm. Source code may be found in KMEANS.CPP. Sample data isfound in KM2.DAT. The KMEANS program accepts input consisting of vectors and calculates the given number of cluster centers using the K-means algorithm. Output is directed to the screen. Usage for KMEANS is: KMEANS SOURCEFILE <enter> The format of the source file is: NPat- Number of patterns (int) SizeVect- Size of vector (int) NClust- Number of cluster centers(int) vect[1,1] ... vect[1,SizeVect]- vector 1 (real) vect[2,1] ... vect[2,SizeVect]- vector 2 (real) . . . . . . vect[NPat,1] ... vect[NClust,SizeVector]- vector N (real) To compile: ICC KMEANS.CPP <enter>
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Author: li |
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Description: 系统聚类算法K-means 属于聚类分析中一种基本的划分方法，常采用误差平方和准则函数作为聚类准则，该算法在处理大数据集时是相对可伸缩且高效率的，同时具有潜在的数据并行性。但是这种算法依赖于初始值的选择以及数据的输入顺序；此外，当运用误差平方和准则函数测度聚类效果时，如果各簇的形状和大小差别很大，为使误差平方和 Jc 值达到最小有可能出现将大的聚类簇分割的现象。-system clustering algorithm K-means cluster analysis is a basic method is often used squared error criterion function as a cluster criteria, the algorithm in handling large data sets are relatively scalable, high-efficient and has the potential of data parallelism. However, this algorithm depends on the initial value of the options and data input sequence; In addition, when using square error of measurement function and the criteria clustering effect, if the cluster size and shape vary greatly, the square error for the Jc value and minimize the possibility of the emergence of large cluster segmentation clustering phenomenon.
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Author: tang |
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Description: 利用聚类算法分割图像，将维对分法只可将图像分为2部分，可以作为二值化的代码,K-均值法可将图像分为任意多部分。程序直接采用R、G、B三色作为特征参数，聚类中心为随机值，当然也可以采用其他参数，程序编译为EXE文件后速度还可以接受，但尚有改进的余地，那位高手有空修改的话，请给我也发份代码。-clustering algorithm using image segmentation, Victoria right method can only image is divided into two parts, the two values can be used as the source, K-means algorithm can be divided into images of arbitrary multi-part. Procedures used directly in R, G, B color as the characteristic parameters for the cluster center random value, of course, can also be used for other parameters, procedures EXE compiler to speed document acceptable, but there is still room for improvement, but the master of the time change, then please give me also made in the code.
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Author: pbt |
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Description: K-Means是聚类分析中重要的一种方法，此源码是K-Means聚类分析的C语言实现。-K- Means clustering analysis is the most important way, this source is K- Means clustering analysis of the C language.
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Author: Owen |
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Description: 编写K-均值聚类算法程序，对下图所示数据进行聚类分析(选k=2)-prepare K-means clustering algorithm, the data shown in the chart below cluster analysis (EAC k = 2)
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Description: How the K-mean Cluster work
Step 1. Begin with a decision the value of k = number of clusters
Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following:
Take the first k training sample as single-element clusters
Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster.
Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample.
Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments. -How the K-mean Cluster workStep 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (Nk) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3. Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4. Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.
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Author: yangdi |
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Description: 聚类算法包,包括K-Means,性能好，有例程-Clustering algorithm package, including K-Means, a good performance, there are routines
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Author: haibo zheng |
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Description: 数据挖掘中的k均值算法，应该属于聚类分析的，c语言版。-Data Mining k-means algorithm, should belong to cluster analysis, c language version.
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Author: 师帅 |
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Description: 空间数据分析中最常用的是聚类分析,而K-MEANS算法是聚类分析中常用的,其主要思想是在给定的聚类数目下对多维(我做的是三维空间点)向量进行聚类,-Spatial data analysis is the most commonly used cluster analysis, while the K-MEANS algorithm is commonly used in cluster analysis, the main idea is to set the number of under the multi-dimensional clustering (I make the three-dimensional space-point) vector cluster,
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Author: tangkezong |
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Description: k均值聚类算法源码
聚类算法学习的实例功能-k-means cluster algorithm
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Author: wueching |
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Description: k-means算法的实现，实用matlab是实现的，可以用啦做聚类分析-k-means algorithm for the realization of the practical realization of matlab, so you can use cluster analysis
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Author: test |
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Description: 聚类方法中的K-means实现，用matlab语言实现的聚类-Clustering of K-means implementation of the cluster with matlab language
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Author: 收到回复 |
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Description: K-means算法是最为经典的基于划分的聚类方法，是十大经典数据挖掘算法之一。K-means算法的基本思想是：以空间中k个点为中心进行聚类，对最靠近他们的对象归类。通过迭代的方法，逐次更新各聚类中心的值，直至得到最好的聚类结果。-K-means algorithm is based on the division of the classic clustering method, is ten classic one of data mining algorithm. K-means the basic idea of the algorithm is: to the space K point as the center of the cluster analysis, near their object classification. Through the iterative method, each successive update clustering center value, until get the best clustering results.
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Author: 彭立军 |
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Description: 基于K-means聚类算法的社团发现方法
先定义了网络中节点关联度,并构建了节点关联度矩阵, 在此基础上给出了一种基于 K-means聚类算法的复杂网络社团发现方法。
以最小关联度原则选取新的聚类中心, 以最大关联度原则进行模式归类,直到所有的节点都划分完为止, 最后根据模块度来确定理想的社团数-K-means clustering algorithm based on the association discovery
To define a network node correlation, and build the node correlation matrix in this basis, given a K-means clustering algorithm based on a complex network of associations that way.
The principle of the minimum correlation to select a new cluster center to the principle of maximum correlation pattern classification until all the nodes are divided until the end, the last under the module to determine the degree of the ideal number of community
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Author: maverick |
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Description: 利用K-means对图像进行聚类，识别。您可以设置参数达到更好的识别效果(Using K-means to cluster and identify images.You can set parameters to achieve better recognition results)
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Author: branden |
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Description: K-means算法是硬聚类算法，是典型的基于原型的目标函数聚类方法的代表，它是数据点到原型的某种距离作为优化的目标函数，利用函数求极值的方法得到迭代运算的调整规则。K-means算法以欧式距离作为相似度测度，它是求对应某一初始聚类中心向量V最优分类，使得评价指标J最小。算法采用误差平方和准则函数作为聚类准则函数。(The K-means algorithm is a hard clustering algorithm, which is representative of the prototype based objective function clustering method. It is the distance from the data point to the prototype as the objective function of the optimization, and the method of using the function to find the extremum is used to get the adjustment rules of the iterative operation. The K-means algorithm takes Euclidean distance as the similarity measure, it is to find the V optimal classification corresponding to an initial cluster center vector, so that the evaluation index J is the smallest. The error square sum criterion function is used as a clustering criterion function.)
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Author: Daizy7 |
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Description: K-means聚类算法的matlab实现(k-means clustering is a method of vector quantization, originally from 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: invoker`Z |
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Description: k means 聚类算法，适用于供应链，物流，选址等配送中心或需求点的筛选聚类(It applies for the field of supply chain, logistics, hub location to cluster the Dcs or demand points.)
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Author: yayahei |
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