Python聚類算法之DBSACN實例分析
本文實例講述了Python聚類算法之DBSACN。分享給大家供大家參考,具體如下:
DBSCAN:是一種簡單的,基于密度的聚類算法。本次實現(xiàn)中,DBSCAN使用了基于中心的方法。在基于中心的方法中,每個數(shù)據(jù)點的密度通過對以該點為中心以邊長為2*EPs的網(wǎng)格(鄰域)內(nèi)的其他數(shù)據(jù)點的個數(shù)來度量。根據(jù)數(shù)據(jù)點的密度分為三類點:
核心點:該點在鄰域內(nèi)的密度超過給定的閥值MinPs。
邊界點:該點不是核心點,但是其鄰域內(nèi)包含至少一個核心點。
噪音點:不是核心點,也不是邊界點。
有了以上對數(shù)據(jù)點的劃分,聚合可以這樣進(jìn)行:各個核心點與其鄰域內(nèi)的所有核心點放在同一個簇中,把邊界點跟其鄰域內(nèi)的某個核心點放在同一個簇中。
# 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")]
# 計算每個數(shù)據(jù)點相鄰的數(shù)據(jù)點,鄰域定義為以該點為中心以邊長為2*EPs的網(wǎng)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)
# 定義鄰域內(nèi)相鄰的數(shù)據(jù)點的個數(shù)大于4的為核心點
MinPts = 5
corePointIdx = [pointIdx for pointIdx,surPointIdxs in surroundPoints.iteritems() if len(surPointIdxs)>=MinPts]
# 鄰域內(nèi)包含某個核心點的非核心點,定義為邊界點
borderPointIdx = []
for pointIdx,surPointIdxs in surroundPoints.iteritems():
if (pointIdx not in corePointIdx):
for onesurPointIdx in surPointIdxs:
if onesurPointIdx in corePointIdx:
borderPointIdx.append(pointIdx)
break
# 噪音點既不是邊界點也不是核心點
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))]
# 各個核心點與其鄰域內(nèi)的所有核心點放在同一個簇中
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]
# 邊界點跟其鄰域內(nèi)的某個核心點放在同一個簇中
for pointidx,surroundIdxs in surroundPoints.iteritems():
for oneSurroundIdx in surroundIdxs:
if (pointidx in borderPointIdx and oneSurroundIdx in corePointIdx):
groups[pointidx] = groups[oneSurroundIdx]
break
# 取簇規(guī)模最大的5個簇
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')
# 打印噪音點,黑色
pl.plot([eachpoint[0] for eachpoint in noisePoint], [eachpoint[1] for eachpoint in noisePoint], 'ok')
pl.show()
運行效果截圖如下:

希望本文所述對大家Python程序設(shè)計有所幫助。
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