clustering python

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Leandro
13 May 2020
1#HIERARCHCAL CLUSTERING
2
3#import the linkage, dendrogram and fcluster func from scipy
4from scipy.cluster.hierarchy import dendrogram,linkage,fcluster
5import matplotlib.pyplot as plt
6import numpy as np
7#Create your linkage object, it contains all the info about the 
8#joins and clusterization
9#The "ward" argument refers to the linking method ("single","average"...)
10Z = linkage(X,"ward")
11#plotting the dendrogram
12plt.figure(figsize = (25,30))
13# Color threshold refers to the distance cutoff for coloring the clusters
14dendrogram(Z, leaf_font_size = 8, color_threshold = 10)
15plt.show()
16# fcluster returns an array as big as your df \w the cluster each data belongs
17# u can cut the clusters using diferent "criterion". Some examples:
18
19# U only want 4 clusters:
20k = 4
21clusters = fcluster(Z,k,criterion="maxclust")
22
23# U want the max distance in a cluster to be 10:
24max_d = 20
25clusters = fcluster(Z,max_d, criterion = "distance")
26
27# Visualization of the clustering (2d clustering)
28plt.figure(figsize = (10,8))
29# now we use the object /w the ncluster info to color the scatter
30# cmap refers to the color palette we are using
31plt.scatter(X[:,0], X[:,1] ,  c = clusters, cmap = "brg")
32plt.show()
Dario
13 Nov 2016
1from sklearn.cluster import KMeans
2kmeans = KMeans(init="random", n_clusters=3, n_init=10, max_iter=300, random_state=42 )
3kmeans.fit(x_train) #Replace your training dataset instead of x_train
4# The lowest SSE value
5print(kmeans.inertia_)
6# Final locations of the centroid
7print(kmeans.cluster_centers_)
8# The number of iterations required to converge
9print(kmeans.n_iter_)
10# first five predicted labels 
11print(kmeans.labels_[:5])
12
13
14# init controls the initialization technique. The standard version of the k-means algorithm is implemented by setting init to "random". Setting this to "k-means++" employs an advanced trick to speed up convergence, which you’ll use later.
15
16# n_clusters sets k for the clustering step. This is the most important parameter for k-means.
17
18# n_init sets the number of initializations to perform. This is important because two runs can converge on different cluster assignments. The default behavior for the scikit-learn algorithm is to perform ten k-means runs and return the results of the one with the lowest SSE.
19
20# max_iter sets the number of maximum iterations for each initialization of the k-means algorithm.
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