Python implementation of K-means clustering

The principle of bioinformatics Operation V-bomb: The realization of K-means clustering.

Reprint please keep the source!

Python implementation of K-means clustering

Principle Reference: K-means cluster (upper)

The data is given by the teacher, two-dimensional, 2 * 3800 data. You can see that there are 7 classes in plot.

How to determine the number of categories I am learning, this script directly to the initial classification, and so I learned to send again.

The Python code is posted below, and the version is Python3.6.

1 #-*-coding:utf-8-*-2 """3 Created on Wed Dec 6 16:01:174 5 @author: Zxzhu6 """7 ImportNumPy as NP8 ImportMatplotlib.pyplot as Plt9  fromNumPyImportRandomTen  One defDistance (x): A     defDis (y): -         returnNP.SQRT (sum (x-y) **2)#European distance -     returnDis the  - defInit_k_means (k): -K_means = {} -      forIinchRange (k): +K_means[i] = [] -     returnK_means +  A defCal_seed (K_mean):#Recalculate seed points atK_mean =Np.array (K_mean) -New_seed = Np.mean (k_mean,axis=0)#the mean values of each dimension -     returnNew_seed -      - defK_means (Data,seed_k,k_means): -      forIinchData: inf =Distance (i) -dis = list (map (f,seed_k))#the distance from all seed points at a certain point toindex =dis.index (min (dis)) + k_means[index].append (i) -      theNew_seed = []#Storing new seeds *      forIinchRange (len (seed_k)): $ new_seed.append (Cal_seed (K_means[i]))Panax NotoginsengNew_seed =Np.array (new_seed) -     returnK_means,new_seed the      + defRun_k_means (data,k): ASeed_k = Data[random.randint (len (data), size=k)]#randomly generated seed points theK_means = Init_k_means (k)#initialize each type of +result =K_means (Data,seed_k,k_means) -Count =0 $      while  not(result[1] = = Seed_k). All ():#seed Point Change, continue clustering $Count+=1 -Seed_k = result[1] -K_means = Init_k_means (k=7)    theresult =K_means (Data,seed_k,k_means) -     Print(' Done')Wuyi     #print (result[1]) the     Print(count) -Plt.figure (figsize= (8,8)) WuColor ='rbgyckm' -      forIinchRange (k): AboutMyData =Np.array (Result[0][i]) $Plt.scatter (Mydata[:,0],mydata[:,1],color =Color[i]) -     returnResult[0] -  -data = Np.loadtxt ('K-means_data') ARun_k_means (data,k=7)   

Enclose the result diagram:

This algorithm is too dependent on the initial seed point selection, random points are likely to get local optimal results, so next learn how to set the initial seed point and the number of categories.

Python implementation of K-means clustering