程式設計實現DBSCAN密度聚類演算法，並以西瓜資料集4.0為例進行聚類效果分析

程式設計實現DBSCAN密度聚類演算法，並以西瓜資料集4.0為例進行聚類效果分析

西瓜資料集4.0：

csv檔案連結: 百度網盤
連結：https://pan.baidu.com/s/1NJmdbm5-3wh6EQUiHEOByA
提取碼：6666

# 記得第一行要加類別標籤
密度,含糖率
0.697,0.46
0.774,0.376
0.634,0.264
0.608,0.318
0.556,0.215
0.403,0.237
0.481,0.149
0.437,0.211
0.666,0.091
0.243,0.267
0.245,0.057
0.343,0.099
0.639,0.161
0.657,0.198
0.36,0.37
0.593,0.042
0.719,0.103
0.359,0.188
0.339,0.241
0.282,0.257
0.748,0.232
0.714,0.346
0.483,0.312
0.478,0.437
0.525,0.369
0.751,0.489
0.532,0.472
0.473,0.376
0.725,0.445
0.446,0.459

程式碼實現:

# ssy_4_DBSCANimport math
import numpy as np
import pandas as pd
import pylab as pl

xigua = pd.read_csv('xigua4.csv')
dataset=[(i[0],i[1]) for i in xigua.values]

#計算歐幾里得距離,a,b分別為兩個元組
def dist(a, b):
    return math.sqrt(math.pow(a[0]-b[0], 2)+math.pow(a[1]-b[1], 2))

#演算法模型
def DBSCAN(D, e, Minpts):
    
    #初始化核心物件集合T,聚類個數k,聚類集合C, 未訪問集合P,
    T = set() 
    k = 0 
    C = []
    P = set(D)
    for d in D:
        if len([ i for i in D if dist(d, i) <= e]) >= Minpts:
            T.add(d)
    #開始聚類
    #所有的核心的 有多個類
    while len(T):
        P_old = P
        #選取一個核心點
        o = list(T)[np.random.randint(0, len(T))]
        #把核心點從未選取中取出
        P = P - set(o)
        Q = []
        Q.append(o)
        while len(Q):
            q = Q[0]
            #核心點周圍的點
            Nq = [i for i in D if dist(q, i) <= e]
            #核心點和周圍的點都記錄下來
            if len(Nq) >= Minpts:
                S = P & set(Nq)
                Q += (list(S))
                P = P - S
            Q.remove(q)
        k += 1
        Ck = list(P_old - P)
        T = T - set(Ck)
        C.append(Ck)
    return C
#畫圖  shanyou_4_DBSCAN
    
def draw(C):
    
    colValue = ['r', 'y', 'g', 'b', 'c', 'k', 'm']
    for i in range(len(C)):
        coo_X = []  #x座標列表
        coo_Y = []  #y座標列表
        for j in range(len(C[i])):
            coo_X.append(C[i][j][0])
            coo_Y.append(C[i][j][1])
        pl.scatter(coo_X, coo_Y, marker='o', color=colValue[i%len(colValue)], label=i+1)
    pl.legend(loc='upper left')
    pl.show()

C = DBSCAN(dataset, 0.11, 5) 
draw(C)

最終聚類結果圖：