Clustering by fast search and find of density
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Clustering by fast search and find of density
-这是science 上2014年5月的一篇文章,主要的思想就是聚类中心点的选择。
在kmeans的方法中,我们随机生成种子点进行迭代。此方法是先用密度峰值来确定聚类中心,确定聚类中心之后,得到距离近的点为同一类。
此方法有两个计算的要点
- 局部密度
- 与高密度点的距离
- matlab实现
clear;clc;cluster = 3;center = 50*randn(3,2);dcc = 10;numOfpoint = 300;for i = 1:numOfpointcenterindex = randperm(3,1);centerx = center(centerindex,1);centery = center(centerindex,2);theta = rand(1)*2*pi;radius = dcc*rand(1);point(i,1) = centerx + radius*cos(theta);point(i,2) = centery + radius*sin(theta);endplot(center(:,1),center(:,2),'r*')hold onplot(point(:,1),point(:,2),'bo')P = [center; point];distance = zeros(size(P,1));% first compute distance of each pairfor i = 1:size(P,1)for j = i+1:size(P,1)distance(i,j) = sqrt(sum((P(i,:)-P(j,:)).^2));distance(j,i) = distance(i,j);endend% compute local density of every pointfor i = 1:size(P,1)density(i,1) = length(find(distance(i,:)<dcc))-1;end%compute deltamaxdensity = find(density == max(density));for i = 1:size(P,1)tempindex = find(density>density(i));if ~isempty(tempindex)delta(i,1) = min(distance(i,tempindex));elsedelta(i,1) = max(distance(i,:));endendfigureplot(density,delta,'ro')
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- Clustering by fast search and find of density peaks翻译
- Clustering by fast search and find of density peaks
- Clustering by fast search and find of density
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