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Description: K均值是一个预先知道类数的算法，需要具备专业知识，不现实。本文提出一个确定类数的方法。-K is a means to know in advance the number of categories algorithm, requires expertise and unrealistic. This paper presents a number of categories to determine the method.
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Size: 31744 |
Author: 李中 |
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Description: 高效的k-means算法实现，使用了k-d树与局部搜索等提高k-means算法的执行效率，同时包含示例代码，用c++代码实现。 Effecient implementation of k-means algorith, k-d tree and local search strategy are implementd to improve the effeciency, samples are included to show how to use it. All codes are implemented in C++.-Efficient k-means algorithm, the use of a kd tree with local search, such as k-means algorithm to improve the implementation efficiency of the sample code included with c++ Code. Effecient implementation of k-means algorith, kd tree and local search strategy are implementd to improve the effeciency, samples are included to show how to use it. All codes are implemented in C++.
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Size: 907264 |
Author: 陈明 |
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Description: 用Java编写的KD TREE算法实现, 希望对大家有所帮助-Java prepared using KD TREE algorithm, I hope all of you to help
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Size: 1024 |
Author: 阎贺 |
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Description: ％ EM algorithm for k multidimensional Gaussian mixture estimation
％
％ Inputs:
％ X(n,d) - input data, n=number of observations, d=dimension of variable
％ k - maximum number of Gaussian components allowed
％ ltol - percentage of the log likelihood difference between 2 iterations ([] for none)
％ maxiter - maximum number of iteration allowed ([] for none)
％ pflag - 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none)
％ Init - structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none)
％
％ Ouputs:
％ W(1,k) - estimated weights of GM
％ M(d,k) - estimated mean vectors of GM
％ V(d,d,k) - estimated covariance matrices of GM
％ L - log likelihood of estimates
％- EM algorithm for k multidimensional Gaussian mixture estimation Inputs: X (n, d)- input data, n = number of observations, d = dimension of variable k- maximum number of Gaussian components allowed ltol- percentage of the log likelihood difference between 2 iterations ([] for none) maxiter- maximum number of iteration allowed ([] for none) pflag- 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none) Init- structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none) Ouputs: W (1, k)- estimated weights of GM M (d, k)- estimated mean vectors of GM V (d, d, k)- estimated covariance matrices of GM L- log likelihood of estimates
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Size: 3072 |
Author: Shaoqing Yu |
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Description: k-d tree 算法介绍，介绍了一种可行的方法，从而实现搜素。-kd tree algorithm, the introduction of a feasible way to achieve the Su-found.
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Size: 191488 |
Author: sunyifeng |
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Description: 首先介绍模糊集基本知识
其次K均值聚类算法(HCM)介绍
最后重点介绍了模糊C均值聚类，
模糊聚类是一种很重要思想，最近再图像处理中就用的了这种思想，也算是一点思维创新-First of all, introduce basic knowledge of fuzzy sets was followed by K-means clustering algorithm (HCM) focuses on the introduction Finally fuzzy C-means clustering, fuzzy clustering is a very important thinking, recently re-image processing on the use of such thinking, but also Innovative thinking is that
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Size: 17408 |
Author: huiguiyang |
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Description: Rob Hess的SIFT算法的C语言实现(基于OpenCV),金字塔采样和高斯差分提取特征点，K-D树寻找同名点，RANSAC去粗差-Rob Hess of the SIFT algorithm C language (based on OpenCV), sampling and Gaussian pyramid differential extraction of feature points, KD tree search for points of the same name, RANSAC to gross errors
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Size: 448512 |
Author: lonfan |
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Description: K-D树源码，不错的空间查找算法，在三维重建和匹配中应用较多！-KD tree source, good space search algorithm, in the three-dimensional reconstruction and matching the application of more!
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Size: 105472 |
Author: yangronghao |
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Description: c++实现的KNN库:建立高维度的K-d tree,实现K邻域搜索，最小半径搜索-K-NN algorithm implementation.
It supports data structures and algorithms for both exact and approximate nearest neighbor searching in arbitrarily high dimensions.
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Size: 624640 |
Author: duckur |
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Description: matlab 代码 k-means 算法 实现2－D数据的聚类-matlab code for k-means algorithm is 2-D data clustering
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Size: 2048 |
Author: 王新民 |
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Description: 本压缩包完整地实现了最小可度限制生成树的算法程序，代码很完整。-The archive can be fully achieved the degree-constrained minimum spanning tree algorithm program, the code is very complete.
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Size: 2048 |
Author: 榔头 |
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Description: 寻找测试样本的最近邻，可以有效的用于用于模式识别，信号处理-This is a small but efficient tool to perform K-nearest neighbor search, which has wide Science and Engineering applications, such as pattern recognition, data mining and signal processing.
The code was initially implemented through vectorization. After discussions with John D Errico, I realized that my algorithm will suffer numerical accurancy problem for data with large values. Then, after trying several approaches, I found simple loops with JIT acceleration is the most efficient solution. Now, the performance of the code is comparable with kd-tree even the latter is coded in a mex file.
The code is very simple, hence is also suitable for beginner to learn knn search.
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Size: 3072 |
Author: 刘晓红 |
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Description: 书中使用主流的程序设计语言C++作为具体的实现语言。
书的内容包括表、栈、队列、树、散列表、优先队列、排序、不相交集算法、图论算法、算法分析
、算法设计、摊还分析、查找树算法、k-d树和配对堆等。 -Book using mainstream programming language C++ as a specific implementation language. The book includes tables, stacks, queues, trees, hash tables, priority queues, sorting, disjoint set algorithm, graph theory, algorithms, algorithm analysis, algorithm design, amortized analysis, search tree algorithms, kd trees and pairing heaps and so on.
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Size: 113664 |
Author: 任鹏富 |
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Description: 本文中在构造关键点的对应时定义了难度D,以反映这种选择的变形效果的优劣。计算难度D时, 引入了B样条和曲率K等数学工具, 使难度值D较合理, 变形效果平滑,程序量约5000 行C 源代码行, 在本文后面给出了实际运行的结果,并对算法的时间代价和适用范围进行了分析。-The key point in constructing this article, the corresponding definition of the degree of difficulty when the D, to reflect the effect of such a choice of deformation of the advantages and disadvantages. The difficulty of calculating D, the introduction of the B-spline and the curvature K and other mathematical tools to make a more reasonable difficulty value of D, deformation effects of smoothing, the program is about 5000-line C source code line, is given later in this paper the results of actual operation, and costs and the time of the algorithm application were analyzed.
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Size: 268288 |
Author: 陈东尧 |
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Description: c语言经典算法
【程序1】
题目：有1、2、3、4个数字，能组成多少个互不相同且无重复数字的三位数？都是多少？
1.程序分析：可填在百位、十位、个位的数字都是1、2、3、4。组成所有的排列后再去
掉不满足条件的排列。
2.程序源代码：
main()
{
int i,j,k
printf("\n")
for(i=1 i<5 i++) ／*以下为三重循环*/
for(j=1 j<5 j++)
for (k=1 k<5 k++)
{
if (i!=k&&i!=j&&j!=k) /*确保i、j、k三位互不相同*/
printf(" d, d, d\n",i,j,k)
}
}
-classical algorithm c language program 1】 【Title: There are numbers 1,2,3,4, how many can be composed of distinct three-digit numbers with no repetition? Is how much? 1. Program Analysis: fill in the hundred, ten, a bit of the numbers are 1,2,3,4. Removed after the composition does not satisfy all the conditions of the arrangement of the arrangement. 2. Program source code: main () {int i, j, k printf (" \ n" ) for (i = 1 i < 5 i++) /* The following is the triple loop*/for (j = 1 j < 5 j++) for (k = 1 k < 5 k++) {if (i! = k & & i! = j & & j! = k) /* ensure i, j, k three distinct*/printf ( " d, d, d \ n" , i, j, k) }}
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Size: 70656 |
Author: zhang |
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Description: function [L,C] = kmeans(X,k)
KMEANS Cluster multivariate data using the k-means++ algorithm.
[L,C] = kmeans(X,k) produces a 1-by-size(X,2) vector L with one class
label per column in X and a size(X,1)-by-k matrix C containing the
centers corresponding to each class.
Version: 07/08/11
Authors: Laurent Sorber (Laurent.Sorber@cs.kuleuven.be)
References:
[1] J. B. MacQueen, "Some Methods for Classification and Analysis of
MultiVariate Observations", in Proc. of the fifth Berkeley
Symposium on Mathematical Statistics and Probability, L. M. L. Cam
and J. Neyman, eds., vol. 1, UC Press, 1967, pp. 281-297.
[2] D. Arthur and S. Vassilvitskii, "k-means++: The Advantages of
Careful Seeding", Technical Report 2006-13, Stanford InfoLab, 2006.
-function [L,C] = kmeans(X,k)
KMEANS Cluster multivariate data using the k-means++ algorithm.
[L,C] = kmeans(X,k) produces a 1-by-size(X,2) vector L with one class
label per column in X and a size(X,1)-by-k matrix C containing the
centers corresponding to each class.
Version: 07/08/11
Authors: Laurent Sorber (Laurent.Sorber@cs.kuleuven.be)
References:
[1] J. B. MacQueen, "Some Methods for Classification and Analysis of
MultiVariate Observations", in Proc. of the fifth Berkeley
Symposium on Mathematical Statistics and Probability, L. M. L. Cam
and J. Neyman, eds., vol. 1, UC Press, 1967, pp. 281-297.
[2] D. Arthur and S. Vassilvitskii, "k-means++: The Advantages of
Careful Seeding", Technical Report 2006-13, Stanford InfoLab, 2006.
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Author: ehsan |
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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
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Size: 2048 |
Author: 赵元 |
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Description: 本程序实现对四维Iris.Data的分类处理，应用K-Means算法将其分为两类-This procedure to realize the four d Iris. The classification of the Data processing, the application of K-Means algorithm which is divided into two categories
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Size: 137216 |
Author: 王丽君 |
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Description: 为非监督分类的一种——K均值算法，这里设定的适用于灰度图像，分类后的图像在显示的同时保存在D盘-A non-supervised classification- K-means algorithm, set applies to gray image, classified image display while stored in the D drive
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Size: 49152 |
Author: lichenming |
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Description: 主要是K-Means算法的实现，可以直接执行，其中图片的载入用到opencv，其他均为C++源码。-Mainly K- Means algorithm implementation, which can be d directly, the images load use opencv, other are c++ source code.
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Size: 2048 |
Author: seven |
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