Course Content

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

What is a Hierarchical Clustering?

In this section, we will consider the hierarchical clustering - one more clustering algorithm.

How does this algorithm work? We will consider the AGNES (Agglomerative Nesting clustering) algorithm. It can be called a bottom-up approach since, in the beginning, all the points are in separate clusters. Then, some clusters are joined based on linkages, until all the necessary number of clusters will be reached.

In Python, the Hierarchical clustering algorithm is implemented within the AgglomerativeClustering function from the sklearn.cluster library. Unlike in the two previous sections, to predict the labels there you need to use the .fit_predict method with data as a parameter. Let's rewrite the necessary actions step by step:

Create an AgglomerativeClustering model object with a necessary number of clusters (n_clusters) and parameters set (will be considered in the next chapters).
Fit the data and predict the labels using the .fit_predict() function passing data as the parameter.

Agglomerative Clustering has many parameters, among them are n_clusters (as in the previous sections), linkage, affinity, and so on... We will consider them in future chapters.

Task

Given the 2-D dataset of points (training dataset). The scatter plot is shown below.

[object Object]

You need to perform a Hierarchical Clustering for this data. Follow the next steps:

Import AgglomerativeClustering function from sklearn.cluster.
Create AgglomerativeClustering object model with 3 clusters.
Apply .fit_predict() method to model with data as a parameter. Add the result as 'prediction' column to data.
Build a scatter plot of data with 'x' column on the x-axis, 'y' column on the y-axis, and each point colored with respect to the 'prediction' column.

Task

Given the 2-D dataset of points (training dataset). The scatter plot is shown below.

[object Object]

You need to perform a Hierarchical Clustering for this data. Follow the next steps:

Import AgglomerativeClustering function from sklearn.cluster.
Create AgglomerativeClustering object model with 3 clusters.
Apply .fit_predict() method to model with data as a parameter. Add the result as 'prediction' column to data.
Build a scatter plot of data with 'x' column on the x-axis, 'y' column on the y-axis, and each point colored with respect to the 'prediction' column.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Section 3. Chapter 1

What is a Hierarchical Clustering?

In this section, we will consider the hierarchical clustering - one more clustering algorithm.

Create an AgglomerativeClustering model object with a necessary number of clusters (n_clusters) and parameters set (will be considered in the next chapters).
Fit the data and predict the labels using the .fit_predict() function passing data as the parameter.

Agglomerative Clustering has many parameters, among them are n_clusters (as in the previous sections), linkage, affinity, and so on... We will consider them in future chapters.

Task

Given the 2-D dataset of points (training dataset). The scatter plot is shown below.

[object Object]

You need to perform a Hierarchical Clustering for this data. Follow the next steps:

Import AgglomerativeClustering function from sklearn.cluster.
Create AgglomerativeClustering object model with 3 clusters.
Apply .fit_predict() method to model with data as a parameter. Add the result as 'prediction' column to data.
Build a scatter plot of data with 'x' column on the x-axis, 'y' column on the y-axis, and each point colored with respect to the 'prediction' column.

Task

Given the 2-D dataset of points (training dataset). The scatter plot is shown below.

[object Object]

You need to perform a Hierarchical Clustering for this data. Follow the next steps:

Import AgglomerativeClustering function from sklearn.cluster.
Create AgglomerativeClustering object model with 3 clusters.
Apply .fit_predict() method to model with data as a parameter. Add the result as 'prediction' column to data.
Build a scatter plot of data with 'x' column on the x-axis, 'y' column on the y-axis, and each point colored with respect to the 'prediction' column.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Section 3. Chapter 1

What is a Hierarchical Clustering?

In this section, we will consider the hierarchical clustering - one more clustering algorithm.

Create an AgglomerativeClustering model object with a necessary number of clusters (n_clusters) and parameters set (will be considered in the next chapters).
Fit the data and predict the labels using the .fit_predict() function passing data as the parameter.

Agglomerative Clustering has many parameters, among them are n_clusters (as in the previous sections), linkage, affinity, and so on... We will consider them in future chapters.

Task

Given the 2-D dataset of points (training dataset). The scatter plot is shown below.

[object Object]

You need to perform a Hierarchical Clustering for this data. Follow the next steps:

Import AgglomerativeClustering function from sklearn.cluster.
Create AgglomerativeClustering object model with 3 clusters.
Apply .fit_predict() method to model with data as a parameter. Add the result as 'prediction' column to data.
Build a scatter plot of data with 'x' column on the x-axis, 'y' column on the y-axis, and each point colored with respect to the 'prediction' column.

Task

Given the 2-D dataset of points (training dataset). The scatter plot is shown below.

[object Object]

You need to perform a Hierarchical Clustering for this data. Follow the next steps:

Import AgglomerativeClustering function from sklearn.cluster.
Create AgglomerativeClustering object model with 3 clusters.
Apply .fit_predict() method to model with data as a parameter. Add the result as 'prediction' column to data.
Build a scatter plot of data with 'x' column on the x-axis, 'y' column on the y-axis, and each point colored with respect to the 'prediction' column.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

In this section, we will consider the hierarchical clustering - one more clustering algorithm.

Create an AgglomerativeClustering model object with a necessary number of clusters (n_clusters) and parameters set (will be considered in the next chapters).
Fit the data and predict the labels using the .fit_predict() function passing data as the parameter.

Agglomerative Clustering has many parameters, among them are n_clusters (as in the previous sections), linkage, affinity, and so on... We will consider them in future chapters.

Task

Given the 2-D dataset of points (training dataset). The scatter plot is shown below.

[object Object]

You need to perform a Hierarchical Clustering for this data. Follow the next steps:

Import AgglomerativeClustering function from sklearn.cluster.
Create AgglomerativeClustering object model with 3 clusters.
Apply .fit_predict() method to model with data as a parameter. Add the result as 'prediction' column to data.
Build a scatter plot of data with 'x' column on the x-axis, 'y' column on the y-axis, and each point colored with respect to the 'prediction' column.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Section 3. Chapter 1

Switch to desktop for real-world practiceContinue from where you are using one of the options below