DBSCAN code C＃或vb.net，聚类分析

问题描述

请我需要你们的支持劝告库或code在vb.net或C＃.NET的应用DBSCAN使数据Denisty的群集。我有一个GPS数据，我想找到留下来使用DBSCAN算法点。但是，我不明白许多算法的技术部分。

Kindly I need your support to advice a library or a code in vb.net or C#.net that applies the DBSCAN to make Denisty Based Cluster of data . I have a GPS data , and I want to find stay points using the DBSCAN algorithm . But , I do not understand much of the technical part of the algorithm.

推荐答案

不知道这就是你要找的内容，因为该算法是非常好上的。你想的算法或它的翻译（或好库）在C＃中的一个交代？

Not sure that's what you're looking for because the algorithm is very well explain on wikipedia. Do you want an explaination of the algorithm or a translation(or good library) of it in C# ?

您可以看看普通聚类算法了。

让说你选择了小量和元素的数量开始集群为4。

Let say you chose epsilon and the number of element to start a cluster is 4.

您需要定义一个距离函数，一个DBSCAN功能和扩展集群功能：

You need to define a distance function, a DBSCAN function and an expand cluster function:

从维基百科：

DBSCAN(D, eps, MinPts)
   C = 0
   for each unvisited point P in dataset D
      mark P as visited
      N = getNeighbors (P, eps)
      if sizeof(N) < MinPts
         mark P as NOISE
      else
         C = next cluster
         expandCluster(P, N, C, eps, MinPts)

expandCluster(P, N, C, eps, MinPts)
   add P to cluster C
   for each point P' in N
      if P' is not visited
         mark P' as visited
         N' = getNeighbors(P', eps)
         if sizeof(N') >= MinPts
            N = N joined with N'
      if P' is not yet member of any cluster
         add P' to cluster C

您点的列表：

第一：选择一个随机点：

在小量测试（小量是圆的半径），如果点的数量是4。如果是启动群集（绿色），否则标记为噪声（红色）:( fonction DBSCAN每个访问过的点）箭头显示您访问过的所有点

Test in epsilon (Epsilon is the radius of the circles) if the number of point is 4. If yes start a cluster (green) otherwise mark as noise (red):(fonction DBSCAN for each unvisited point)The arrows show all the points you visited

二：展开集群：一旦你找到一个簇标记所有点绿，在这一组检查更多的积分

secondly: Expand cluster : once you find a cluster mark all the point green and check for more points in this cluster

注意：一原噪声点可以在集群变为绿色，如果

2红点实际上是在一个集群中...

the 2 red point are actually in a cluster ...

一旦您完成所有的点去你停止

Once you went through all the points you stop

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