Matlab和Python聚类算法代码包含dbscan和kmeans算法

# python实现 dbscan # 参考：https://blog.csdn.net/FAICULTY/article/details/79430164 # 计算两个向量之间的欧氏距离 import random import matplotlib.pyplot as plt import numpy as np from sklearn.datasets import make_blobs # 用于生成测试数据 # 计算两个向量之间的欧式距离 def calDist(X1, X2): sum = 0 for x1, x2 in zip(X1, X2): sum += (x1 - x2) ** 2 return sum ** 0.5 # 获取一个点的ε-邻域（记录的是索引） def getNeibor(data, dataSet, e): res = [] for i in range(np.shape(dataSet)[0]): if calDist(data, dataSet[i]) < e: res.append(i) return res # 密度聚类算法 def DBSCAN(dataSet, e, minPts): coreObjs = {} # 初始化核心对象集合 C = {} n = np.shape(dataSet)[0] # 找出所有核心对象，key是核心对象的index，value是ε-邻域中对象的index for i in range(n): neibor = getNeibor(dataSet[i], dataSet, e) if len(neibor) >= minPts: coreObjs[i] = neibor oldCoreObjs = coreObjs.copy() k = 0 # 初始化聚类簇数 notAccess = list(range(n)) # 初始化未访问样本集合（索引） while len(coreObjs) > 0: OldNotAccess = [] OldNotAccess.extend(notAccess) cores = coreObjs.keys() # 随机选取一个核心对象 randNum = random.randint(0, len(cores) - 1) cores = list(cores) core = cores[randNum] queue = [] queue.append(core) notAccess.remove(core) while len(queue) > 0: q = queue[0] del queue[0] if q in oldCoreObjs.keys(): delte = [val for val in oldCoreObjs[q] if val in notAccess] # Δ = N(q)∩Γ queue.extend(delte) # 将Δ中的样本加入队列Q notAccess = [val for val in notAccess if val not in delte] # Γ = Γ\Δ k += 1 C[k] = [val for val in OldNotAccess if val not in notAccess] for x in C[k]: if x in coreObjs.keys(): del coreObjs[x] return C def draw(C, dataSet): color = ['r', 'y', 'g', 'b', 'c', 'k', 'm'] for i in C.keys(): X = [] Y = [] datas = C[i] for j in range(len(datas)): X.append(dataSet[datas[j]][0]) Y.append(dataSet[datas[j]][1]) plt.scatter(X, Y, marker='o', color=color[i % len(color)], label=i) plt.legend(loc='upper right') plt.show() def loadDataSet(): X, y = make_blobs(n_samples=1000, n_features=2, centers=[[-1, -1], [0, 0], [1, 1], [2, 2]], cluster_std=[0.4, 0.2, 0.2, 0.2], random_state=9) data = X return data if __name__ == '__main__': dataSet = loadDataSet() print(dataSet) C = DBSCAN(dataSet, 0.2, 5) draw(C, dataSet)