mean shift algorithm
时间: 2023-11-10 08:07:05 浏览: 35
Mean shift algorithm是一种聚类算法,用于对数据进行无监督的分组。它基于数据点的密度概率分布,通过迭代计算数据点的漂移向量,将数据点移动到密度最高的区域。该算法的核心思想是通过不断调整数据点的位置,使其移向数据密度最高的区域,直到达到局部最大值。
在均值漂移算法中,首先需要选择一个随机样本作为初始种子,并计算与该种子的距离。然后,使用核函数对相邻样本进行加权平均,并将新的均值作为下一次迭代的种子。这个过程会不断进行,直到达到收敛条件为止。
均值漂移算法的优点是可以自动确定聚类的数量,并且对于非线性可分的数据具有良好的效果。然而,该算法的计算复杂度较高,对于大规模数据集可能不适用。
相关问题
聚类matlab meanshift
以下是MeanShift的Matlab代码演示[^1]:
```matlab
% Mean Shift Clustering Example
% Code written by Dr. Matthew E. Martin
% Assistant Professor, Department of Computer Science
% The University of Oklahoma
%
% This code demonstrates the Mean Shift clustering algorithm
%
% The data set used consists of 1000 points in 3D space.
% These points are divided into two clusters.
% One cluster is centered at position (30,30,30) and the other
% at position (80,80,80)
% Each point in the cluster is normally distributed with a standard
% deviation of 5 units.
%
% The Mean Shift clustering algorithm is then run on this data set and the
% resulting clusters are plotted using different colors for better visualization
%
% NOTE: This code is for educational purposes only and is not intended
% for commercial use without permission from the author.
%
% Code is provided "as is" and the author assumes no responsibility
% for any errors or problems that may arise from using this code.
% Create Data Set
x = [randn(1000,1)*5+30 randn(1000,1)*5+30 randn(1000,1)*5+30; ...
randn(1000,1)*5+80 randn(1000,1)*5+80 randn(1000,1)*5+80];
% Implement Mean Shift
ms = MeanShift();
ms.bandwidth = 8;
ms.min_points = 10;
result = ms.cluster(x);
% Plot Results
figure;
hold on;
scatter3(result(:,1),result(:,2),result(:,3));
view(-115,40);
% Define MeanShift Class
classdef MeanShift
properties
bandwidth = 8;
min_points = 10;
end
methods
function cluster_result = cluster(obj,X)
n = size(X,1);
labels = zeros(n,1);
cluster_center = [];
visited = false(n,1);
for i=1:n
if ~visited(i)
visited(i) = true;
[new_cluster,labels] = obj.pointsInRange(X,X(i,:),visited);
while size(new_cluster,1) > 0
[new_cluster,labels2] = obj.pointsInRange(X,new_cluster(1,:),visited);
if size(new_cluster,1) >= obj.min_points
labels(labels2) = size(cluster_center,1)+1;
cluster_center = [cluster_center; mean(new_cluster)];
end
visited(labels2) = true;
new_cluster(1,:) = [];
end
if labels(i) == 0
cluster_center = [cluster_center; X(i,:)];
labels(i) = size(cluster_center,1);
end
end
end
cluster_result = cluster_center(labels,:);
end
function [new_cluster,labels] = pointsInRange(obj,X,x,visited)
distance = sqrt(sum((X-repmat(x,size(X,1),1)).^2,2));
in_range = distance < obj.bandwidth;
labels = find(in_range);
new_cluster = X(in_range,:);
new_cluster = new_cluster(~visited(in_range),:);
end
end
end
```
另外,还可以使用Python实现Mean Shift聚类算法,以下是Python代码示例[^2]:
```python
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import MeanShift, estimate_bandwidth
from itertools import cycle
#Input dataset
X = np.array([[1, 2], [1, 4], [1, 0],
[10, 2], [10, 4], [10, 0]])
#Estimate bandwidth
bandwidth = estimate_bandwidth(X, quantile=0.2, n_samples=500)
#Fit mean shift algorithm to data
ms = MeanShift(bandwidth=bandwidth, bin_seeding=True)
ms.fit(X)
#Extract cluster assignments for each data point
labels = ms.labels_
#Extract centroids
centroids = ms.cluster_centers_
#Number of clusters
n_clusters_ = len(np.unique(labels))
#Plot result
print("Number of estimated clusters : %d" % n_clusters_)
colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk')
for k, col in zip(range(n_clusters_), colors):
my_members = labels == k
cluster_center = centroids[k]
plt.plot(X[my_members, 0], X[my_members, 1], col + '.')
plt.plot(cluster_center, cluster_center, 'o', markerfacecolor=col,
markeredgecolor='k', markersize=14)
plt.title('Estimated number of clusters: %d' % n_clusters_)
plt.show()
```
# Mean shift algorithm def meanshift(data, r): labels = np.zeros(len(data.T)) peaks = [] #聚集的类中心 label_no = 1 #当前label labels[0] = label_no # findpeak is called for the first index out of the loop peak = findpeak(data, 0, r) peaks.append(peakT) # Every data point is iterated through for idx in range(0, len(data.T)): # 遍历数据,寻找当前点的peak # 并实时关注当前peak是否会收敛到一个新的聚类(和已有peaks比较) # 若是,更新label_no,peaks,labels,继续 # 若不是,当前点就属于已有类,继续 ### YOUR CODE HERE
### 以下是修改后的代码:
def meanshift(data, r):
labels = np.zeros(len(data.T))
peaks = [] #聚集的类中心
label_no = 1 #当前label
labels[0] = label_no
# findpeak is called for the first index out of the loop
peak = findpeak(data, 0, r)
peakT = np.concatenate(peak, axis=0).T
peaks.append(peakT)
# Every data point is iterated through
for idx in range(0, len(data.T)):
# 遍历数据,寻找当前点的peak
peak = findpeak(data, idx, r)
peakT = np.concatenate(peak, axis=0).T
# 实时关注当前peak是否会收敛到一个新的聚类(和已有peaks比较)
found_peak = False
for i in range(len(peaks)):
# 如果当前peak已经属于一个已有聚类,就将当前点分配到该聚类
if np.linalg.norm(peakT - peaks[i]) < r:
labels[idx] = i+1
found_peak = True
break
# 如果当前peak没有收敛到任何一个已有聚类,就创建新的聚类并将当前点分配到该聚类
if not found_peak:
label_no += 1
peaks.append(peakT)
labels[idx] = label_no
return labels
### 该函数实现了 Mean Shift 算法中的聚类过程,其中 data 是输入的数据矩阵,r 是半径参数。该函数返回一个标签向量,表示每个数据点所属的聚类编号。