Python聚类算法之DBSACN实例分析

本文实例讲述了Python聚类算法之DBSACN。分享给大家供大家参考，具体如下：

DBSCAN：是一种简单的，基于密度的聚类算法。本次实现中，DBSCAN使用了基于中心的方法。在基于中心的方法中，每个数据点的密度通过对以该点为中心以边长为2*EPs的网格(邻域)内的其他数据点的个数来度量。根据数据点的密度分为三类点:

核心点：该点在邻域内的密度超过给定的阀值MinPs。
边界点：该点不是核心点，但是其邻域内包含至少一个核心点。
噪音点：不是核心点，也不是边界点。

有了以上对数据点的划分，聚合可以这样进行：各个核心点与其邻域内的所有核心点放在同一个簇中，把边界点跟其邻域内的某个核心点放在同一个簇中。

    # scoding=utf-8
    import pylab as pl
    from collections import defaultdict,Counter
    points = [[int(eachpoint.split("#")[0]), int(eachpoint.split("#")[1])] for eachpoint in open("points","r")]
    # 计算每个数据点相邻的数据点，邻域定义为以该点为中心以边长为2*EPs的网格
    Eps = 10
    surroundPoints = defaultdict(list)
    for idx1,point1 in enumerate(points):
      for idx2,point2 in enumerate(points):
        if (idx1 < idx2):
          if(abs(point1[0]-point2[0])<=Eps and abs(point1[1]-point2[1])<=Eps):
            surroundPoints[idx1].append(idx2)
            surroundPoints[idx2].append(idx1)
    # 定义邻域内相邻的数据点的个数大于4的为核心点
    MinPts = 5
    corePointIdx = [pointIdx for pointIdx,surPointIdxs in surroundPoints.iteritems() if len(surPointIdxs)>=MinPts]
    # 邻域内包含某个核心点的非核心点，定义为边界点
    borderPointIdx = []
    for pointIdx,surPointIdxs in surroundPoints.iteritems():
      if (pointIdx not in corePointIdx):
        for onesurPointIdx in surPointIdxs:
          if onesurPointIdx in corePointIdx:
            borderPointIdx.append(pointIdx)
            break
    # 噪音点既不是边界点也不是核心点
    noisePointIdx = [pointIdx for pointIdx in range(len(points)) if pointIdx not in corePointIdx and pointIdx not in borderPointIdx]
    corePoint = [points[pointIdx] for pointIdx in corePointIdx] 
    borderPoint = [points[pointIdx] for pointIdx in borderPointIdx]
    noisePoint = [points[pointIdx] for pointIdx in noisePointIdx]
    # pl.plot([eachpoint[0] for eachpoint in corePoint], [eachpoint[1] for eachpoint in corePoint], 'or')
    # pl.plot([eachpoint[0] for eachpoint in borderPoint], [eachpoint[1] for eachpoint in borderPoint], 'oy')
    # pl.plot([eachpoint[0] for eachpoint in noisePoint], [eachpoint[1] for eachpoint in noisePoint], 'ok')
    groups = [idx for idx in range(len(points))]
    # 各个核心点与其邻域内的所有核心点放在同一个簇中
    for pointidx,surroundIdxs in surroundPoints.iteritems():
      for oneSurroundIdx in surroundIdxs:
        if (pointidx in corePointIdx and oneSurroundIdx in corePointIdx and pointidx < oneSurroundIdx):
          for idx in range(len(groups)):
            if groups[idx] == groups[oneSurroundIdx]:
              groups[idx] = groups[pointidx]
    # 边界点跟其邻域内的某个核心点放在同一个簇中
    for pointidx,surroundIdxs in surroundPoints.iteritems():
      for oneSurroundIdx in surroundIdxs:
        if (pointidx in borderPointIdx and oneSurroundIdx in corePointIdx):
          groups[pointidx] = groups[oneSurroundIdx]
          break
    # 取簇规模最大的5个簇
    wantGroupNum = 3
    finalGroup = Counter(groups).most_common(3)
    finalGroup = [onecount[0] for onecount in finalGroup]
    group1 = [points[idx] for idx in xrange(len(points)) if groups[idx]==finalGroup[0]]
    group2 = [points[idx] for idx in xrange(len(points)) if groups[idx]==finalGroup[1]]
    group3 = [points[idx] for idx in xrange(len(points)) if groups[idx]==finalGroup[2]]
    pl.plot([eachpoint[0] for eachpoint in group1], [eachpoint[1] for eachpoint in group1], 'or')
    pl.plot([eachpoint[0] for eachpoint in group2], [eachpoint[1] for eachpoint in group2], 'oy')
    pl.plot([eachpoint[0] for eachpoint in group3], [eachpoint[1] for eachpoint in group3], 'og')
    # 打印噪音点，黑色
    pl.plot([eachpoint[0] for eachpoint in noisePoint], [eachpoint[1] for eachpoint in noisePoint], 'ok')  
    pl.show()

运行效果截图如下：

希望本文所述对大家Python程序设计有所帮助。