# 初识聚类算法: DBSACN

intergret 发布于 2012年10月22日 21时, 4评/5331阅

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:

详见博客：http://blog.sina.com.cn/s/blog_62186b460101ard2.html

## 代码片段(2)[全屏查看所有代码]

### 1. [代码][Python]代码     跳至 [1] [全屏预览]

```# 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()```

## 发表评论 回到顶部 网友评论(4)

•  1楼：小小小新 发表于 2013-12-29 14:46 哪句话是导入数据的？
•  2楼：HaiziGe 发表于 2016-03-10 21:52 楼主，源数据能分享一下吗？这是我的邮箱707895571@qq.com，非常感谢~
•  3楼：马富天 发表于 2016-05-29 13:07 同求数据源~~邮箱335134463@qq.com
•  4楼：马富天 发表于 2016-05-29 13:14 楼主，请问能否分享一下数据源呢~

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