Python implementation K-Mean algorithm example (K-means clustering algorithm) _python

Simple realization of the point k mean of the plane, using Euclidean distance, and Pylab display.

Copy Code code as follows:

Import Pylab as Pl

#calc Euclid Squire
Def Calc_e_squire (A, B):
Return (a[0]-b[0]) * * 2 + (a[1]-b[1) **2

#init the Point
A = [2,4,3,6,7,8,2,3,5,6,12,10,15,16,11,10,19,17,16,13]
b = [5,6,1,4,2,4,3,1,7,9,16,11,19,12,15,14,11,14,11,19]

#define TWO K_value
K1 = [6,3]
K2 = [6,1]

#defint Tow Cluster
SSE_K1 = []
Sse_k2 = []
while True:
    sse_k1 = []
  & nbsp SSE_K2 = []
    for I in range:
        e_squire1 = calc_e_ Squire (K1, [a[i], B[i]]
        e_squire2 = Calc_e_squire (K2, [A[i], b[i])
        if (e_squire1 <= e_squire2):
             sse_k1.append (i)
        else:
            sse_k2.append (i)

    # Change K_value
    k1_x = SUM ([a[i] to I in SSE_K1])/len (SSE_K1)
    k1_y = SUM ([b[ I] for i in SSE_K1])/len (SSE_K1)

k2_x = SUM ([a[i] for I in SSE_K2])/len (SSE_K2)
k2_y = SUM ([b[i] for I in SSE_K2])/len (SSE_K2)

If K1!= [k1_x, k1_y] or K2!= [k2_x, k2_y]:
K1 = [k1_x, k1_y]
K2 = [K2_x, k2_y]
Else
Break

kv1_x = [A[i] for i in SSE_K1]
Kv1_y = [B[i] for i in SSE_K1]

kv2_x = [A[i] for i in SSE_K2]
Kv2_y = [B[i] for i in SSE_K2]

Pl.plot (kv1_x, kv1_y, ' O ')
Pl.plot (kv2_x, kv2_y, ' or ')

Pl.xlim (1, 20)
Pl.ylim (1, 20)
Pl.show ()