Perform DBSCAN Clustering | Basic Clustering Algorithms
Cluster Analysis

Course Content

Cluster Analysis

## Cluster Analysis

1. What is Clustering?
2. Basic Clustering Algorithms
3. How to choose the best model?

# Perform DBSCAN Clustering

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:

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

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:

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

Everything was clear?

Section 2. Chapter 7

# Perform DBSCAN Clustering

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:

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

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:

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

Everything was clear?

Section 2. Chapter 7

# Perform DBSCAN Clustering

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:

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

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:

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

Everything was clear?

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