搜索资源列表
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基于粒子群的改进K均值聚类算法源代码。适用于MATLAB7.1。-Improved PSO-based K means clustering algorithm source code. For MATLAB7.1.
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使用c-均值聚类算法在IRIS数据上进行聚类分析，随机选择三个初始聚类中心，经过多次迭代，最终将150个样本分为三类。-Use c-means clustering algorithm in the IRIS data on the cluster analysis, three randomly chosen initial cluster centers, through a series of iterative, 150 samples will eventually fall into
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层次模糊聚类的算法实现，一个用C或者C++写的kmeans算法，最新的聚类算法-Level fuzzy clustering algorithm, one using C or C++ written kmeans algorithm, the latest clustering algorithm
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一种基于自适应按需加权分簇算法的CBRP路由协议研究-Based on adaptive on-demand weighted clustering algorithm for routing protocol research CBRP
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蚁群聚类算法及其源码。是蚁群算法跟聚类算法的结合，启发式搜索社团结构。-Ant colony clustering algorithm and its source code. Ant colony algorithm with the clustering algorithm is a combination of heuristic search community structure.
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经典浓度聚类算法OPTICS，matlab实现，简单易懂，可以运行-Classic Concentration clustering algorithm OPTICS, matlab implementation, easy to understand, you can run
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经典层次聚类算法DIANA，MATLAB编程，简单易懂，可以运行-Classical hierarchical clustering algorithm DIANA, MATLAB programming, easy to understand, you can run
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DBSCAN算法的matlab 实现。。基于密度的聚类算法-DBSCAN algorithm matlab implementation. . Density-based clustering algorithm. . .
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模糊聚类算法，K-means and K-medoid algorithms，Fuzzy C-means algorithm，The Gustafson{Kessel algorithm，The Gath{Geva algorithm-Fuzzy clustering algorithm, K-means and K-medoid algorithms, Fuzzy C-means algorithm, The Gustafson (Kessel algorithm, The Gath (Gev
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spectral-clustering-demo 谱聚类的matlab演示程序，是学习谱聚类的好资料，经调试通过，包含n-cut和改进的n-cut-spectral-clustering-demo demo matlab spectral clustering, spectral clustering is good information to learn by debugging through, including the n-cut and improving the n-cut
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用matlab实现K均值聚类算法，里面有详细的英文备注和举例，可以看明白-Achieved using K means clustering algorithm matlab
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珍藏版，可实现，新K均值聚类算法，分为如下几个步骤：
一、初始化聚类中心
1、根据具体问题，凭经验从样本集中选出C个比较合适的样本作为初始聚类中心。
2、用前C个样本作为初始聚类中心。
3、将全部样本随机地分成C类，计算每类的样本均值，将样本均值作为初始聚类中心。
二、初始聚类
1、按就近原则将样本归入各聚类中心所代表的类中。
2、取一样本，将其归入与其最近的聚类中心的那一类中，重新计算样本均值，更新聚类中心。然后取下一样本，
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clustring(Density-Based Spatial Clustering of Applications with Noise) is a data clustering algorithm proposed by Martin Ester, Hans-Peter Kriegel, Jö rg Sander and Xiaowei Xu in 1996.[1] It is a density-based clustering algorithm because it find
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包含了几乎所有的聚类算法，用matlab实现的。里面有demo示例，可直接运行。-Contains almost all of the clustering algorithm, implemented using matlab. There are demo example, can be directly run.
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基于聚类算法和模式识别的人脸检测程序原代码-Based on clustering algorithm and pattern recognition face detection procedures the original code
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用matlabr2009编程实现神经网络模糊聚类算法，采用cell工具把整个程序分为以下几个部分-Programming neural network with matlabr2009 fuzzy clustering algorithm, using cell tools to the entire program is divided into the following sections
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本文通过介绍新的聚类算法到传感器节点调度算法中，从而实现网络覆盖的最优化-This paper describes a new clustering algorithm to the sensor node scheduling algorithm in order to achieve optimal network coverage
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An Fast and efficient spectral clustering algorithm, which could perform fast and efficient spectral clustering process.
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K均值算法使用的聚类准则函数的误差平方和准则，通过反复迭代优化聚类结果，使所有样本到各自所属类别的中心的距离平方和达到最小。-K-means clustering algorithm uses squared error criterion function and criteria through iterative optimization clustering result, all the samples to the respective classes of the center s
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网络入侵聚类神经网络的聚类算法程序，可作为学习参考。-Network Intrusion clustering neural network clustering algorithm, can be used as learning reference.
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