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Optics聚类算法 OPTICS没有显示地产生一个数据集合簇，它为自动和交互地聚类分析计算一个簇次序。这个次序代表了数据基于密度地聚类结构。它包含地信息，等同于从一个宽广地参数设置范围所获得的基于密度的聚类-Optics do not show clustering algorithm OPTICS to produce a collection of data clusters, it is automatically and interactively computing cluster
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A general technique for the recovery of signi cant
image features is presented. The technique is based on
the mean shift algorithm, a simple nonparametric pro-
cedure for estimating density gradients. Drawbacks of
the current methods (includi
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Form1.cs是应用聚类算法DBSCAN (Density-Based Spatical Clustering of Application with Noise)的示例，可以通过两个参数EPS和MinPts调节聚类。
DBSCAN.cs是实现文件，聚类算法的进一步信息请参考“数据挖掘”或者相关书籍
聚类示例数据来自于sxdb.mdb，一个Access数据库-Form1.cs is the application of clustering algorithm DBSCAN (Dens
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L3_1.m: 純量量化器的設計(程式)
L3_2.m: 量化造成的假輪廓(程式)
L3_3.m: 向量量化器之碼簿的產生(程式)
L3_4.m: 利用LBG訓練三個不同大小與維度的碼簿並分別進行VQ(程式)
gau.m: ML量化器設計中分母的計算式(函式)
gau1.m: ML量化器設計中分子的計算式(函式)
LBG.m: LBG訓練法(函式)
quantize.m:高斯機率密度函數的非均勻量化(函式)
VQ.m: 向量量化(函式)
L3
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In statistics, a mixture model is a probabilistic model for density estimation using a mixture distribution. A mixture model can be regarded as a type of unsupervised learning or clustering. Mixture models should not be confused with models for compo
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这是一种基于密度的聚类分析算法，可以发现任意形状的簇，可以发现噪声点。-This is a density-based clustering analysis algorithm can find clusters of arbitrary shape can be found noise points.
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改进的Salama网络拓扑随机生成算法通用Matlab程序，在随机抛撒节点的时候使用了K均值聚类，网络节点分布均匀且疏密得当，边的分布也比较均衡。-Improved algorithm for randomly generated network topology Salama General Matlab program, when the nodes in the random throw using K means clustering, the network node density
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Matlab --- --- --- --- --- --- --- --- --- --- --- --- -
Function: [class,type]=dbscan(x,k,Eps)
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Aim:
Clustering the data with Density-Based Scan Algorithm with Noi
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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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Density-Based Spatial Clustering of Applications with Noise (or DBSCAN) is an algorithm used in cluster analysis which is described in this Wikipedia article (http://en.wikipedia.org/wiki/DBSCAN).
The basic idea of cluster analysis is to partit
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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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Density Conscious Subspace Clustering for High-Dimensional Data
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this is a density based algorithm
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基于密度的聚类算法是一个比较有代表性的基于密度的聚类算法。与划分和层次聚类方法不同，它将簇定义为密度相连的点的最大集合，能够把具有足够高密度的区域划分为簇，并可在噪声的空间数据库中发现任意形状的聚类-density based clustering
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基于快速搜索数据密度峰值的聚类算法是一种基于聚类中心具有较近邻点有更高密度且其与更高密度点间有着较大的相对距离的一类算法。-Clustering by fast search and find of density peaks is based on the idea that cluster centers are characterized by a higher density than their neighbors and by a relatively large distance
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基于核密度估计的层次聚类算法 -hierarchcial clustering with kernel density estimation
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2014年发表在Science中的一篇文章Clustering by fast search and find of density peaks，其中还包括了作者用到的数据集和MATLAB源程序-2014 published an article in Science Clustering by fast search and find of density peaks, which also includes the author used data sets and MATLAB sourc
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这是密度峰聚类的简易代码，比原作者给出的代码要简洁迅速。并提供RI指数检测。用matlab编程的代码-
这是密度峰聚类的简易代码，比原作者给出的代码要简洁迅速。并提供RI指数检测。用matlab编程的代码
This is a simple density peak clustering code, compared to the original author of the code to be concise and rapid. And provides RI index de
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对clustering by fast research and density peaks程序做了小改动-clustering by fast research and density peaks
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clustering using density ratio
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