Python_data_science_第11课

K-means clustering

1. K-Means clustering is unsupervised learning. means split data into K groups, algorithm for k-means clustering:
   • Randomly pick K centroids(k-means)
   • Assign each data point to the centroid it is closest to.
   • Recompute the centroids based on the average position of each centroid’s points.
   • Iterate until points stop changing assignment to centroids.
   • Predict the cluster for new points.

   The limitation of K-means clustering:

• Choosing K: choose the right value of K, the principal way of choosing k is just start low and keep increasing the value of K depending on how many groups you want.
• Avoiding local minima
• Labeling the clusters.

1. 实际例子，可以使用sklearn里面的KMeans函数。
   这个例子制造了一些人收入和年龄的数字，然后进行cluster.

    In [1]: from numpy import random , array 
   
    In [2]: def createClusteredData(N, k):
       ...:     random.seed(10)
       ...:     pointsPerCluster = float(N) / k 
       ...:     x = []
       ...:     for i in range(k):
       ...:         incomeCentroid = random.uniform(20000.0, 200000.0)
       ...:         ageCentroid = random.uniform(20.0, 70.0)
       ...:         for j in range(int(pointsPerCluster)):
       ...:             x.append([random.normal(incomeCentroid, 10000.0), random.nor
       ...: mal(ageCentroid, 2.0)])
       ...:     x = array(x)
       ...:     return x
       ...: 
   
    In [3]: from sklearn.cluster import KMeans
   
    In [4]: import matplotlib.pyplot as plt
   
    In [5]: from sklearn.preprocessing import scale
   
    In [6]: from numpy import random, float
   
    In [7]: data = createClusteredData(100, 5)
   
    In [8]: model = KMeans(n_clusters=5)
   
    In [9]: model = model.fit(scale(data))
   
    In [10]: print(model.labels_)
    [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
     1 1 1 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3
     3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2]
   
    In [11]: plt.figure(figsize=(8, 6))
    Out[11]: <matplotlib.figure.Figure at 0x7f6377123a58>
   
    In [13]: plt.scatter(data[:, 0], data[:, 1], c=model.labels_.astype(float))
    Out[13]: <matplotlib.collections.PathCollection at 0x7f6375a1a908>
   
    In [14]: plt.show()