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

What is a Spectral Clustering?

Welcome to the fourth section of the course! In this section, we will learn the last (but not the last overall) clustering method.

This method is heavily based on math, such as eigenvalues, laplacians, graphs, and kernels. We will not dig into the algorithm itself but will consider the main cases of its usage.

You might forget all the charts you saw in the course, but in the second section, we considered an interesting set of points. Look at the scatterplot below.

And look below at the results of the K-Means algorithm.

Well, that's not what we were looking for. So, how can you implement the spectral clustering algorithm in Python? The key function is SpectralClustering from sklearn.cluster. The algorithm of prediction is the same as in the previous section:

Create SpectralClustering model with k clusters using n_clusters = k parameter.
Fit the numerical data and predict the labels using the .fit_predict() method of model and passing numerical data as a parameter.

Then, if needed, you can visualize the results.

Let's find out can the algorithm heavily based on strong math correctly define the clusters?

Завдання

Import SpectralClustering function from sklearn.cluster.
Create a SpectralClustering model with 2 clusters named model.
Fit the data and predict the labels using model. Save predicted labels within the 'prediction' column of the data.
Build a scatter plot (using seaborn) with 'x' column on the x-axis, 'y' column on the y-axis, and each point colored with respect to the 'prediction' column of the data.

Завдання

Import SpectralClustering function from sklearn.cluster.
Create a SpectralClustering model with 2 clusters named model.
Fit the data and predict the labels using model. Save predicted labels within the 'prediction' column of the data.
Build a scatter plot (using seaborn) with 'x' column on the x-axis, 'y' column on the y-axis, and each point colored with respect to the 'prediction' column of the data.

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

Все було зрозуміло?

Секція 4. Розділ 1

What is a Spectral Clustering?

Welcome to the fourth section of the course! In this section, we will learn the last (but not the last overall) clustering method.

This method is heavily based on math, such as eigenvalues, laplacians, graphs, and kernels. We will not dig into the algorithm itself but will consider the main cases of its usage.

You might forget all the charts you saw in the course, but in the second section, we considered an interesting set of points. Look at the scatterplot below.

And look below at the results of the K-Means algorithm.

Create SpectralClustering model with k clusters using n_clusters = k parameter.
Fit the numerical data and predict the labels using the .fit_predict() method of model and passing numerical data as a parameter.

Then, if needed, you can visualize the results.

Let's find out can the algorithm heavily based on strong math correctly define the clusters?

Завдання

Import SpectralClustering function from sklearn.cluster.
Create a SpectralClustering model with 2 clusters named model.
Fit the data and predict the labels using model. Save predicted labels within the 'prediction' column of the data.
Build a scatter plot (using seaborn) with 'x' column on the x-axis, 'y' column on the y-axis, and each point colored with respect to the 'prediction' column of the data.

Завдання

Import SpectralClustering function from sklearn.cluster.
Create a SpectralClustering model with 2 clusters named model.
Fit the data and predict the labels using model. Save predicted labels within the 'prediction' column of the data.
Build a scatter plot (using seaborn) with 'x' column on the x-axis, 'y' column on the y-axis, and each point colored with respect to the 'prediction' column of the data.

Все було зрозуміло?

Секція 4. Розділ 1

What is a Spectral Clustering?

Welcome to the fourth section of the course! In this section, we will learn the last (but not the last overall) clustering method.

This method is heavily based on math, such as eigenvalues, laplacians, graphs, and kernels. We will not dig into the algorithm itself but will consider the main cases of its usage.

You might forget all the charts you saw in the course, but in the second section, we considered an interesting set of points. Look at the scatterplot below.

And look below at the results of the K-Means algorithm.

Create SpectralClustering model with k clusters using n_clusters = k parameter.
Fit the numerical data and predict the labels using the .fit_predict() method of model and passing numerical data as a parameter.

Then, if needed, you can visualize the results.

Let's find out can the algorithm heavily based on strong math correctly define the clusters?

Завдання

Import SpectralClustering function from sklearn.cluster.
Create a SpectralClustering model with 2 clusters named model.
Fit the data and predict the labels using model. Save predicted labels within the 'prediction' column of the data.
Build a scatter plot (using seaborn) with 'x' column on the x-axis, 'y' column on the y-axis, and each point colored with respect to the 'prediction' column of the data.

Завдання

Import SpectralClustering function from sklearn.cluster.
Create a SpectralClustering model with 2 clusters named model.
Fit the data and predict the labels using model. Save predicted labels within the 'prediction' column of the data.
Build a scatter plot (using seaborn) with 'x' column on the x-axis, 'y' column on the y-axis, and each point colored with respect to the 'prediction' column of the data.

Все було зрозуміло?

Welcome to the fourth section of the course! In this section, we will learn the last (but not the last overall) clustering method.

This method is heavily based on math, such as eigenvalues, laplacians, graphs, and kernels. We will not dig into the algorithm itself but will consider the main cases of its usage.

You might forget all the charts you saw in the course, but in the second section, we considered an interesting set of points. Look at the scatterplot below.

And look below at the results of the K-Means algorithm.

Create SpectralClustering model with k clusters using n_clusters = k parameter.
Fit the numerical data and predict the labels using the .fit_predict() method of model and passing numerical data as a parameter.

Then, if needed, you can visualize the results.

Let's find out can the algorithm heavily based on strong math correctly define the clusters?

Завдання

Import SpectralClustering function from sklearn.cluster.
Create a SpectralClustering model with 2 clusters named model.
Fit the data and predict the labels using model. Save predicted labels within the 'prediction' column of the data.
Build a scatter plot (using seaborn) with 'x' column on the x-axis, 'y' column on the y-axis, and each point colored with respect to the 'prediction' column of the data.

Секція 4. Розділ 1