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A-NOVEL-SKIN-YCBCR-COLOR-SPACE
- This paper presents a new human skin color model in YCbCr color space and its application to human face detection. Skin colors are modeled by a set of three Gaussian clusters, each of which is characterized by a
rc4
- 实现RC4加密算法，其是密钥长度可变的流加密算法簇。之所以称其为簇，是由于其核心部分的S-box长度可为任意，但一般为256字节。该算法的速度可以达到DES加密的10倍左右。RC4算法的原理很简单，包括初始化算法和伪随机子密码生成算法两大部分。-RC4 encryption algorithm to achieve, it is the variable key length stream cipher algorithm clust
fcmC
- 在原始的 fcm 算法基础上，对算法中的聚类数 c 和加权指数 m 给出优选方法， 进而而出了 fcm 参数优选自适应算法，通过人造数据与具有实际背景的数据验证可以看出 该算法是有效的，该算法不但可以自适应的给出最佳的聚类数，而且可以验证聚类的有效性， 达到最佳聚类的目的-Fcm algorithm in the original, based on the number of clustering algorithms a
Cluster_from_Scratch
- Creating Active/Passive and Active/Active Clusters on Fedora 12
PostgreSQL
- 论述了使用MSVC 开发Windows 平台PostgreSQL 数据库集群的技术和基本方法，包括高性能集群和负载均 衡集群，并详细阐述了该技术的实现。-Discusses the Windows platform using MSVC PostgreSQL database cluster development techniques and basic methods, including high-performance cl
ptscext
- 该程序用蒙特卡罗仿真了群点散射体的体散射-Main program for the Monte Carlo simulations of volumetric scattering by clusters of point scatterers
An-Improved-Ant-Colony-Clustering-Algorithm-Based
- Focused on the disadvantage of classical Euclidian distance in data clustering analysis, we propose an improved distance calculation formula, which describes the local compactness and global connectivity between da
Mining-e-Coomerce-data-to-analyze-customer-behavi
- Mining the E-commerce Data to Analyze the Target Customer Behavior is developed in Java. It clusters customer segments by using K-Means algorithm and data from web log of various e-commerce websites. Consequently, the re
Mail_DB_Report
- 邮件管理,包括数据库和报表。可以实现集群、优化邮件收取等-E-mail management, including databases and reports. Clusters can be achieved, such as optimizing e-mail received
cluster
- J2EE集群相关内容，对J2EE中集群的概念作了全面的介绍，并提供J2EE集群实现机制的说明-J2EE cluster related content, the concept of cluster of J2EE provides a comprehensive introduction and provides the mechanism that J2EE Clusters
k-means--source-code
- k-means算法，已附数据文件。其中文件第一行的3个数字分别为数据数量、数据维度和所需聚类数-k-means algorithm, is attached data files. Which documents the first line of three figures were the amount of data, data dimension and the required number of clusters
mkclean-win32.v0.6.0
- mkclean is a command line tool to clean and optimize Matroska (.mkv / .mka / .mks / .mk3d) and WebM (.webm / .weba) files that have already been muxed. It reorders the elements with the Cues at the front, so your Matrosk
dunns
- FCM的一个有效性函数，经典的Dunn有效性函数，可以自动确定聚类数目-FCM of a validity function, the classic Dunn validity function, can automatically determine the number of clusters
FCM
- FCM算法是一种基于划分的聚类算法，它的思想就是使得被划分到同一簇的对象之间相似度最大，而不同簇之间的相似度最小。模糊C均值算法是普通C均值算法的改进，普通C均值算法对于数据的划分是硬性的，而FCM则是一种柔性的模糊划分。-FCM algorithm is a clustering algorithm based on division, the idea is to make it to the same cluster is div
Energy-Efficient-Routing-Protocol-for-Wireless.zi
- The proposed routing protocol for Wireless Sensor Network is hierarchical and cluster based. The protocol supports mobility in the Sensor Nodes as well as in the Sink. The entire protocol is described in terms of two p
MARK_Kmeans
- 使用k-means算法对一副RGB色彩空间的图像作简单的聚类。根据命令行提示输入聚类的大小K，程序自动计算每一个像素点的归属并着色该点为该类的色彩均值。工程运行于VS2008环境，需要OpenCV支持。Debug目下exe文件可以直接双击运行查看结果。-Using k-means algorithm on an RGB color space images to make a simple clustering. According t
D17499GC10
- Oracle Database 10g Real Application Clusters Student Guide • Volume I&II D17499GC10-Oracle Database 10g Real Application Clusters Student Guide • Volume I&II D17499GC10
segmentation
- The Mean Shift algorithm clusters an n-dimensional data set (i.e., each data point is described by a feature vector of n values) by associating each point with a peak of the data set’s probability density.
luster
- 模糊聚类分析最佳聚类数的确定方法研究Fuzzy clustering analysis method to determine the optimal number of clusters of research-Fuzzy clustering analysis method to determine the optimal number of clusters of research
OPTICS-algorithm---Wikipedia--the-free-encycloped
- OPTICS ("Ordering Points To Identify the Clustering Structure") is an algorithm for finding density-based clusters in spatial data. It was presented by Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel and Jö r