Python for Data Science

Chapter 4 - Clustering Models

Segment 2 - Hierarchical methods

Hierarchical Clustering

Hierarchical clustering methods predict subgroups within data by finding the distance between each data point and its nearest neighbors, and then linking the most nearby neighbors.

The algorithm uses the distance metric it calculates to predict subgroups.

To guess the number of subgroups in a dataset, first look at a dendrogram visualization of the clustering results.

Hierarchical Clustering Dendrogram

Dendrogram: a tree graph that's useful for visually displaying taxonomies, lineages, and relatedness

Hierarchical Clustering Use Cases

• Hospital Resource Management
• Customer Segmentation
• Business Process Management
• Social Network Analysis

Hierarchical Clustering Parameters

Distance Metrics

• Euclidean
• Manhattan
• Cosine

Linkage Parameters

• Ward
• Complete
• Average

Parameter selection method: use trial and error

Setting up for clustering analysis

import numpy as np
import pandas as pd

import matplotlib.pyplot as plt
from pylab import rcParams
import seaborn as sb

import sklearn
import sklearn.metrics as sm

from sklearn.cluster import AgglomerativeClustering

import scipy
from scipy.cluster.hierarchy import dendrogram, linkage
from scipy.cluster.hierarchy import fcluster
from scipy.cluster.hierarchy import cophenet
from scipy.spatial.distance import pdist

np.set_printoptions(precision=4, suppress=True)
plt.figure(figsize=(10, 3))
%matplotlib inline
plt.style.use('seaborn-whitegrid')

address = '~/Data/mtcars.csv'

cars = pd.read_csv(address)
cars.columns = ['car_names','mpg','cyl','disp', 'hp', 'drat', 'wt', 'qsec', 'vs', 'am', 'gear', 'carb']

X = cars[['mpg','disp','hp','wt']].values

y = cars.iloc[:,(9)].values

Using scipy to generate dendrograms

Z = linkage(X, 'ward')

dendrogram(Z, truncate_mode='lastp', p=12, leaf_rotation=45., leaf_font_size=15, show_contracted=True)

plt.title('Truncated Hierarchial Clustering Diagram')
plt.xlabel('Cluster Size')
plt.ylabel('Distance')

plt.axhline(y=500)
plt.axhline(y=100)
plt.show()

Generating hierarchical clusters

k = 2

Hclustering = AgglomerativeClustering(n_clusters=k, affinity='euclidean', linkage='ward')
Hclustering.fit(X)

sm.accuracy_score(y, Hclustering.labels_)

0.78125

Hclustering = AgglomerativeClustering(n_clusters=k, affinity='euclidean', linkage='average')
Hclustering.fit(X)

sm.accuracy_score(y, Hclustering.labels_)

0.78125

Hclustering = AgglomerativeClustering(n_clusters=k, affinity='manhattan', linkage='average')
Hclustering.fit(X)

sm.accuracy_score(y, Hclustering.labels_)

0.71875