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。
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