Python聚类算法之DBSACN实例分析_Python

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：

Python聚类算法之DBSACN实例分析

希望本文所述对大家Python程序设计有所帮助。

Python聚类算法之DBSACN实例分析

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