词向量的K均值聚类(300维) [英] K- Means clustering for word vector (300 dimension)

问题描述

我正在编写一个程序，需要对该程序对200多个300个元素的数组的数据集应用K-means聚类. 有人可以给我提供代码解释的链接吗? 1.通过肘法求k 2.应用k均值方法并获得质心的数组

I am writing a program for which I need to apply K-means clustering over a data set of some >200, 300-element arrays. Could someone provide me with a link to code with explanations on- 1. finding the k through the elbow method 2. applying the k means method and getting the arrays for the centroids

我自己搜索了上面的内容，但没有找到清楚的代码说明. P.s.我正在Google Colab上工作，因此，如果有相同的特定方法，请提出建议

I have searched for the above on my own but have not found any with clear explanations of the code. P.s. I am working on Google Colab, so if there are specific methods for the same, do suggest

我尝试了以下代码，但是，我不断收到以下错误-

I tried the below code, however, I keep getting the following error-

---------------------------------------------------------------------------

TypeError                                 Traceback (most recent call last)

TypeError: float() argument must be a string or a number, not 'list'


The above exception was the direct cause of the following exception:

ValueError                                Traceback (most recent call last)

<ipython-input-70-68e300fd4bf8> in <module>()
     24 
     25 # step 1: find optimal k (number of clusters)
---> 26 find_best_k()
     27 

3 frames

/usr/local/lib/python3.6/dist-packages/numpy/core/_asarray.py in asarray(a, dtype, order)
     83 
     84     """
---> 85     return array(a, dtype, copy=False, order=order)
     86 
     87 

ValueError: setting an array element with a sequence.

推荐答案

假设有12个样本，每个样本具有以下两个功能:

Suppose there are 12 samples each with two features as below:

data=np.array([[1,1],[1,2],[2,1.5],[4,5],[5,6],[4,5.5],[5,5],[8,8],[8,8.5],[9,8],[8.5,9],[9,9]])

您可以使用弯头法和簇中心找到最佳簇数，如下例所示:

You can find the optimal number of clusters using elbow method and the centers of clusters as the following example:

import numpy as np
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt

data=np.array([[1,1],[1,2],[2,1.5],[4,5],[5,6],[4,5.5],[5,5],[8,8],[8,8.5],[9,8],[8.5,9],[9,9]])

def find_best_k():
    sum_of_squared_distances = []
    K=range(1,8) # change 8 in your data 
    for k in K:
        km=KMeans(n_clusters=k)
        km=km.fit(data)
        sum_of_squared_distances.append(km.inertia_)
    plt.plot(K, sum_of_squared_distances, 'bx-')
    plt.xlabel('k')
    plt.ylabel('sum_of_squared_distances')
    plt.title('Elbow method for optimal k')
    plt.show()  
    #The plot looks like an arm, and the elbow on the arm is optimal k.

# step 1: find optimal k (number of clusters)
find_best_k()

def run_kmeans(k,data): # k is the optimal number of clusters
    km=KMeans(n_clusters=k) 
    km=km.fit(data)
    centroids = km.cluster_centers_  #get the center of clusters
    #print(centroids)
    return centroids

def plotresults():
    centroids=run_kmeans(3,data)     
    plt.plot(data[0:3,0],data[0:3,1],'ro',data[3:7,0],data[3:7,1],'bo',data[7:12,0],data[7:12,1],'go')
    for i in range(3):
        plt.plot(centroids[i,0],centroids[i,1],'k*')
        plt.text(centroids[i,0],centroids[i,1], "c"+str(i), fontsize=12)
plotresults()

手肘图:

结果:

希望这会有所帮助.

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