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 DBSCAN Clustering

Завдання

As we mentioned in the previous chapter, DBSCAN algorithm classifies points as core, border, and noise. As a result, we can use this algorithm to clean our data from outliers. Let's create DBSCAN model, clean data, and look at the results.

Your task is to train DBSCAN model on the circles dataset, detect noise points, and remove them. Look at the visualization and compare data before and after cleaning. You have to:

Import the DBSCAN class from sklearn.cluster module.
Use DBSCAN class and .fit() method of this class.
Use .labels_ attribute of DBSCAN class.
Specify clustering.labels_==-1 to detect noise.

Все було зрозуміло?

Секція 2. Розділ 7

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 DBSCAN Clustering

Завдання

Your task is to train DBSCAN model on the circles dataset, detect noise points, and remove them. Look at the visualization and compare data before and after cleaning. You have to:

Import the DBSCAN class from sklearn.cluster module.
Use DBSCAN class and .fit() method of this class.
Use .labels_ attribute of DBSCAN class.
Specify clustering.labels_==-1 to detect noise.

Все було зрозуміло?

Секція 2. Розділ 7