Зміст курсу

Cluster Analysis in Python

1. K-Means Algorithm

Exploratory Data Analysis What is a K-Means Algorithm?Defining the Number of Clusters Case 1: Three Distinct Clusters Case 2: Four Distinct Clusters Clustering Weather Data February vs July Average Temperatures Visualizing the Dynamics Across Clusters

2. K-Medoids Algorithm

What is a K-Medoids Algorithm?Cluster Centers Silhouette Scores K-Medoids and the Weather Data Mean Yearly Temperatures Across Clusters Comparing the Dynamics

3. Hierarchical Clustering

What is a Hierarchical Clustering?Dendrograms Setting Parameters: Linkage Rand Index The Weather Data and Linkages Deciding the Number of Clusters Weather Data: Complete and Ward Linkages How Similar are the Results?

4. Spectral Clustering

What is a Spectral Clustering?Peculiarity of Spectral Clustering Setting Parameters: Affinity Defining the Number of Clusters Is 4 the Optimal Number of Clusters?Comparing the Trends Across Clusters

Defining the Number of Clusters

As we mentioned before, there are no correct solutions to unsupervised learning problems. We can predict the number of clusters by watching the scatter plot, but in real life, data usually has more than 2 dimensions. For example, if you have 4 columns, then there are 6 possible 2-D scatter plots. And you do not want to spend your time watching all of the charts.

In the previous chapter, we mentioned that the K-Means algorithm runs until the variance between the points within each cluster is minimized. So, it sounds like quality metrics, doesn't it? But not everything is so simple. If you put each point in a separate cluster, then all the variances will be zeros, since the variance of the constant (which is a single point) is zero. Well, in that case, clustering makes no sense at all. So, how should we choose what number of clusters we want to consider?

Probably the simplest approach is to build a line plot representing the variances for each number of clusters. For example, let's build such a plot for the data from the previous chapter. Below is the scatter plot of data points.

The desired variances can be extracted by applying the .inertia_ method to the KMeans object after fitting. We will use for loop to iterate over the range object which will represent a different number of clusters and add the variance value to the list.

In this and future chapters, we will use the range() function to generate the list of the integer numbers. We will pass two parameters within this function: start and end - this will generate integer numbers from start to end - 1.


              12345678910111213141516171819202122
            
# Import the libraries
import pandas as pd
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import seaborn as sns

# Read the data
data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/138ab9ad-aa37-4310-873f-0f62abafb038/model_data1.csv')

# Creating lists
clusters = range(2, 9)
variances = []

# Fitting model to different number of clusters
for i in clusters:
    model = KMeans(n_clusters = i)
    model.fit(data)
    variances.append(model.inertia_)
    
# Building lineplot for variances
sns.lineplot(x = clusters, y = variances)
plt.show()

So, how should you interpret this chart? It is believed that the optimal number of clusters is the number at which the decrease of variance is much less than in the previous steps. In the chart above there was a significant drop between 2 and 3, and much less between 3 and 4. After 4 there are no such significant drops that were left to 4. So we confirmed that the optimal number of clusters for the points above is 3. Now it's your turn!

Завдання

Given the 2-D set of points data. The scatter plot visualizing the distribution is below.

You need to build the same line plot representing the dependence of the total within sum of squares vs the number of clusters. Follow the next steps:

Import KMeans from sklearn.cluster.
Create a range object with integer numbers from 2 to 8 and save it within the clusters variable.
Iterate over all the values of clusters. Within the for loop:

Create a KMeans model object with the number of clusters i assigned to model.
Fit the data to model.
Add .inertia_ attribute of model to variances list. This will add the value of the total within sum of squares.

Build seaborn lineplot with clusters on the x-axis, and variances on the y-axis. Do not forget to apply .show() method of plt!

Завдання

Given the 2-D set of points data. The scatter plot visualizing the distribution is below.

You need to build the same line plot representing the dependence of the total within sum of squares vs the number of clusters. Follow the next steps:

Import KMeans from sklearn.cluster.
Create a range object with integer numbers from 2 to 8 and save it within the clusters variable.
Iterate over all the values of clusters. Within the for loop:

Create a KMeans model object with the number of clusters i assigned to model.
Fit the data to model.
Add .inertia_ attribute of model to variances list. This will add the value of the total within sum of squares.

Build seaborn lineplot with clusters on the x-axis, and variances on the y-axis. Do not forget to apply .show() method of plt!

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Все було зрозуміло?

Секція 1. Розділ 3

Defining the Number of Clusters

In this and future chapters, we will use the range() function to generate the list of the integer numbers. We will pass two parameters within this function: start and end - this will generate integer numbers from start to end - 1.


              12345678910111213141516171819202122
            
# Import the libraries
import pandas as pd
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import seaborn as sns

# Read the data
data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/138ab9ad-aa37-4310-873f-0f62abafb038/model_data1.csv')

# Creating lists
clusters = range(2, 9)
variances = []

# Fitting model to different number of clusters
for i in clusters:
    model = KMeans(n_clusters = i)
    model.fit(data)
    variances.append(model.inertia_)
    
# Building lineplot for variances
sns.lineplot(x = clusters, y = variances)
plt.show()

Завдання

Given the 2-D set of points data. The scatter plot visualizing the distribution is below.

You need to build the same line plot representing the dependence of the total within sum of squares vs the number of clusters. Follow the next steps:

Import KMeans from sklearn.cluster.
Create a range object with integer numbers from 2 to 8 and save it within the clusters variable.
Iterate over all the values of clusters. Within the for loop:

Create a KMeans model object with the number of clusters i assigned to model.
Fit the data to model.
Add .inertia_ attribute of model to variances list. This will add the value of the total within sum of squares.

Build seaborn lineplot with clusters on the x-axis, and variances on the y-axis. Do not forget to apply .show() method of plt!

Завдання

Given the 2-D set of points data. The scatter plot visualizing the distribution is below.

You need to build the same line plot representing the dependence of the total within sum of squares vs the number of clusters. Follow the next steps:

Import KMeans from sklearn.cluster.
Create a range object with integer numbers from 2 to 8 and save it within the clusters variable.
Iterate over all the values of clusters. Within the for loop:

Create a KMeans model object with the number of clusters i assigned to model.
Fit the data to model.
Add .inertia_ attribute of model to variances list. This will add the value of the total within sum of squares.

Build seaborn lineplot with clusters on the x-axis, and variances on the y-axis. Do not forget to apply .show() method of plt!

Все було зрозуміло?

Секція 1. Розділ 3

Defining the Number of Clusters

In this and future chapters, we will use the range() function to generate the list of the integer numbers. We will pass two parameters within this function: start and end - this will generate integer numbers from start to end - 1.


              12345678910111213141516171819202122
            
# Import the libraries
import pandas as pd
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import seaborn as sns

# Read the data
data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/138ab9ad-aa37-4310-873f-0f62abafb038/model_data1.csv')

# Creating lists
clusters = range(2, 9)
variances = []

# Fitting model to different number of clusters
for i in clusters:
    model = KMeans(n_clusters = i)
    model.fit(data)
    variances.append(model.inertia_)
    
# Building lineplot for variances
sns.lineplot(x = clusters, y = variances)
plt.show()

Завдання

Given the 2-D set of points data. The scatter plot visualizing the distribution is below.

You need to build the same line plot representing the dependence of the total within sum of squares vs the number of clusters. Follow the next steps:

Import KMeans from sklearn.cluster.
Create a range object with integer numbers from 2 to 8 and save it within the clusters variable.
Iterate over all the values of clusters. Within the for loop:

Create a KMeans model object with the number of clusters i assigned to model.
Fit the data to model.
Add .inertia_ attribute of model to variances list. This will add the value of the total within sum of squares.

Build seaborn lineplot with clusters on the x-axis, and variances on the y-axis. Do not forget to apply .show() method of plt!

Завдання

Given the 2-D set of points data. The scatter plot visualizing the distribution is below.

You need to build the same line plot representing the dependence of the total within sum of squares vs the number of clusters. Follow the next steps:

Import KMeans from sklearn.cluster.
Create a range object with integer numbers from 2 to 8 and save it within the clusters variable.
Iterate over all the values of clusters. Within the for loop:

Create a KMeans model object with the number of clusters i assigned to model.
Fit the data to model.
Add .inertia_ attribute of model to variances list. This will add the value of the total within sum of squares.

Build seaborn lineplot with clusters on the x-axis, and variances on the y-axis. Do not forget to apply .show() method of plt!

Все було зрозуміло?

In this and future chapters, we will use the range() function to generate the list of the integer numbers. We will pass two parameters within this function: start and end - this will generate integer numbers from start to end - 1.


              12345678910111213141516171819202122
            
# Import the libraries
import pandas as pd
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import seaborn as sns

# Read the data
data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/138ab9ad-aa37-4310-873f-0f62abafb038/model_data1.csv')

# Creating lists
clusters = range(2, 9)
variances = []

# Fitting model to different number of clusters
for i in clusters:
    model = KMeans(n_clusters = i)
    model.fit(data)
    variances.append(model.inertia_)
    
# Building lineplot for variances
sns.lineplot(x = clusters, y = variances)
plt.show()

Завдання

Given the 2-D set of points data. The scatter plot visualizing the distribution is below.

You need to build the same line plot representing the dependence of the total within sum of squares vs the number of clusters. Follow the next steps:

Import KMeans from sklearn.cluster.
Create a range object with integer numbers from 2 to 8 and save it within the clusters variable.
Iterate over all the values of clusters. Within the for loop:

Create a KMeans model object with the number of clusters i assigned to model.
Fit the data to model.
Add .inertia_ attribute of model to variances list. This will add the value of the total within sum of squares.

Build seaborn lineplot with clusters on the x-axis, and variances on the y-axis. Do not forget to apply .show() method of plt!

Секція 1. Розділ 3