Cluster Analysis

1. What is Clustering?

First of all, let's understand what is clustering, why we need it, and consider some unique concepts about clustering problems. We will also look at the classification of clustering algorithms according to the principle of their work.

Formulation Of The Problem

What Is Unique About Clustering?

Types Of Clustering

2. Basic Clustering Algorithms

Now let's look at the most popular clustering algorithms in more detail: we will understand the principles of their work, look at their implementation in Python, and have some practice in solving simple tasks using these algorithms.

K-means Clustering

Perform K-means Clustering

Agglomerative Clustering

Perform Agglomerative Clustering

Mean Shift Clustering

DBSCAN Clustering

Perform DBSCAN Clustering

Using Clustering On Real Data

3. How to choose the best model?

Finally, we will understand how to choose the best model and solve real tasks based on evaluation metrics and other validation techniques.

Internal Evaluation

External Evaluation

Quality Evaluation

Perform K-means Clustering

Завдання

Let's check the efficiency of the algorithm on different types of clusters. Now we will use the three built-in datasets of the sklearn library and try to use the K-means algorithm to cluster the corresponding points. We will provide visualizations and try to estimate the quality of clustering using these visualizations.

Your task is to use the K-means clustering algorithm and to solve 3 different clustering problems. Compare the results and make conclusions about clustering quality. You have to:

Use KMeans class from cluster module for import.
Use KMeans class to instantiate a class object
Use.fit()method to train model.
Use .labels_attribute to extract fitted clusters.

_{Once you've completed this task, click the button below the code to check your solution.}

Note
In visualizations, it is necessary to look not at the color of clusters, but at the relative position of points in real and predicted clusters (Python can color the same clusters with different colors in different pictures due to implementation features)

Все було зрозуміло?

Секція 2. Розділ 2

Зміст курсу

Cluster Analysis

1. What is Clustering?

Formulation Of The Problem What Is Unique About Clustering?Types Of Clustering

2. Basic Clustering Algorithms

K-means Clustering Perform K-means Clustering Agglomerative Clustering Perform Agglomerative Clustering Mean Shift Clustering DBSCAN Clustering Perform DBSCAN Clustering Using Clustering On Real Data

3. How to choose the best model?

Internal Evaluation External Evaluation Quality Evaluation

Cluster Analysis

Perform K-means Clustering

Завдання

Your task is to use the K-means clustering algorithm and to solve 3 different clustering problems. Compare the results and make conclusions about clustering quality. You have to:

Use KMeans class from cluster module for import.
Use KMeans class to instantiate a class object
Use.fit()method to train model.
Use .labels_attribute to extract fitted clusters.

_{Once you've completed this task, click the button below the code to check your solution.}

Note
In visualizations, it is necessary to look not at the color of clusters, but at the relative position of points in real and predicted clusters (Python can color the same clusters with different colors in different pictures due to implementation features)

Все було зрозуміло?

Секція 2. Розділ 2