1.读取csv数据做dbscan分析
读取csv文件中相应的列，然后进行转化，处理为本算法需要的格式，然后进行dbscan运算，目前公开的代码也比较多，本文根据公开代码修改，
具体代码如下：
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from sklearn import datasetsimport numpy as npimport randomimport matplotlib.pyplot as pltimport timeimport copyimport pandas as pd# from sklearn.datasets import load_iris def find_neighbor(j, x, eps): N = list() for i in range(x.shape[0]): temp = np.sqrt(np.sum(np.square(x[j] - x[i]))) # 计算欧式距离 if temp <= eps: N.append(i) return set(N) def DBSCAN(X, eps, min_Pts): k = -1 neighbor_list = [] # 用来保存每个数据的邻域 omega_list = [] # 核心对象集合 gama = set([x for x in range(len(X))]) # 初始时将所有点标记为未访问 cluster = [-1 for _ in range(len(X))] # 聚类 for i in range(len(X)): neighbor_list.append(find_neighbor(i, X, eps)) if len(neighbor_list[-1]) >= min_Pts: omega_list.append(i) # 将样本加入核心对象集合 omega_list = set(omega_list) # 转化为集合便于操作 while len(omega_list) > 0: gama_old = copy.deepcopy(gama) j = random.choice(list(omega_list)) # 随机选取一个核心对象 k = k + 1 Q = list() Q.append(j) gama.remove(j) while len(Q) > 0: q = Q[0] Q.remove(q) if len(neighbor_list[q]) >= min_Pts: delta = neighbor_list[q] & gama deltalist = list(delta) for i in range(len(delta)): Q.append(deltalist[i]) gama = gama - delta Ck = gama_old - gama Cklist = list(Ck) for i in range(len(Ck)): cluster[Cklist[i]] = k omega_list = omega_list - Ck return cluster # X = load_iris().datadata = pd.read_csv("testdata.csv")x,y=data['Time (sec)'],data['Height (m HAE)']print(type(x))n=len(x)x=np.array(x)x=x.reshape(n,1)y=np.array(y)y=y.reshape(n,1)X = np.hstack((x, y))cluster_std=[[.1]], random_state=9) eps = 0.08min_Pts = 5begin = time.time()C = DBSCAN(X, eps, min_Pts)end = time.time()plt.figure()plt.scatter(X[:, 0], X[:, 1], c=C)plt.show() |
2.输出结果显示
修改参数显示：
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eps = 0.8min_Pts = 5 |
3.计算效率
采用少量数据计算的时候效率问题不明显，随着数据量增大，计算效率问题就变得尤为明显，难以满足大量数据的计算需求了，后期将想办法优化计算方法或者收集C++代码进行优化了。
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原文链接：https://blog.csdn.net/soderayer/article/details/124089170
