dbscan的matlab源代码
时间: 2023-07-25 15:04:04 浏览: 51
以下是一个简单的DBSCAN算法的Matlab源代码示例:
```matlab
function [clusters, noise] = dbscan(X, epsilon, MinPts)
% 初始化聚类标签和核心点集合
clusters = zeros(size(X, 1), 1);
clusterIdx = 1;
corePoints = [];
% 计算每个样本点的邻域
distances = pdist2(X, X);
neighbors = distances <= epsilon;
% 找到核心点
for i = 1:size(X, 1)
if sum(neighbors(i, :)) >= MinPts
corePoints = [corePoints i];
end
end
% 聚类操作
for i = corePoints
if clusters(i) == 0
expandCluster(X, neighbors, i, clusterIdx, epsilon, MinPts, clusters);
clusterIdx = clusterIdx + 1;
end
end
% 处理噪音点
noise = find(clusters == 0);
end
function expandCluster(X, neighbors, pointIdx, clusterIdx, epsilon, MinPts, clusters)
% 将当前点标记为当前聚类
clusters(pointIdx) = clusterIdx;
% 找到当前点的邻域
seeds = find(neighbors(pointIdx, :));
% 迭代扩展当前聚类
while ~isempty(seeds)
currentPoint = seeds(1);
if clusters(currentPoint) == 0
clusters(currentPoint) = clusterIdx;
% 检查当前点是否是核心点,并将其邻域加入种子集合
if sum(neighbors(currentPoint, :)) >= MinPts
seeds = [seeds setdiff(find(neighbors(currentPoint, :)), seeds)];
end
end
seeds = seeds(2:end);
end
end
```
这是一个基本的DBSCAN算法实现,输入参数包括数据集X,邻域半径epsilon和最小邻域点数MinPts。函数返回聚类结果clusters和噪音点索引noise。请注意,此代码没有处理输入数据的预处理或可视化聚类结果的功能,可以根据需要进行相应的修改和补充。