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

How Similar are the Results?

Well done! Let's look at the last line charts you built in the previous chapter.

As you can see, only the ward linkage could catch the 'downward up to July' trend. Both results are different. But let's find out how different they are using the rand index.

Task

Compute the rand index to compare the results of using complete and ward linkages. Follow the next steps:

Import functions needed:

rand_score from sklearn.metrics.
AgglomerativeClustering from sklearn.cluster.

Create two models model_complete and model_ward performing a hierarchical clustering with 4 clusters both and 'complete' and 'ward' linkages respectively.
Fit the 3-14 columns of data to models and predict the labels. Save the labels for model_complete within labels_complete and for model_ward within labels_ward.
Compute the rand index using labels_complete and labels_ward.

Task

Compute the rand index to compare the results of using complete and ward linkages. Follow the next steps:

Import functions needed:

rand_score from sklearn.metrics.
AgglomerativeClustering from sklearn.cluster.

Create two models model_complete and model_ward performing a hierarchical clustering with 4 clusters both and 'complete' and 'ward' linkages respectively.
Fit the 3-14 columns of data to models and predict the labels. Save the labels for model_complete within labels_complete and for model_ward within labels_ward.
Compute the rand index using labels_complete and labels_ward.

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

Everything was clear?

Section 3. Chapter 8

How Similar are the Results?

Well done! Let's look at the last line charts you built in the previous chapter.

As you can see, only the ward linkage could catch the 'downward up to July' trend. Both results are different. But let's find out how different they are using the rand index.

Task

Compute the rand index to compare the results of using complete and ward linkages. Follow the next steps:

Import functions needed:

rand_score from sklearn.metrics.
AgglomerativeClustering from sklearn.cluster.

Create two models model_complete and model_ward performing a hierarchical clustering with 4 clusters both and 'complete' and 'ward' linkages respectively.
Fit the 3-14 columns of data to models and predict the labels. Save the labels for model_complete within labels_complete and for model_ward within labels_ward.
Compute the rand index using labels_complete and labels_ward.

Task

Compute the rand index to compare the results of using complete and ward linkages. Follow the next steps:

Import functions needed:

rand_score from sklearn.metrics.
AgglomerativeClustering from sklearn.cluster.

Create two models model_complete and model_ward performing a hierarchical clustering with 4 clusters both and 'complete' and 'ward' linkages respectively.
Fit the 3-14 columns of data to models and predict the labels. Save the labels for model_complete within labels_complete and for model_ward within labels_ward.
Compute the rand index using labels_complete and labels_ward.

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

Everything was clear?

Section 3. Chapter 8

How Similar are the Results?

Well done! Let's look at the last line charts you built in the previous chapter.

As you can see, only the ward linkage could catch the 'downward up to July' trend. Both results are different. But let's find out how different they are using the rand index.

Task

Compute the rand index to compare the results of using complete and ward linkages. Follow the next steps:

Import functions needed:

rand_score from sklearn.metrics.
AgglomerativeClustering from sklearn.cluster.

Create two models model_complete and model_ward performing a hierarchical clustering with 4 clusters both and 'complete' and 'ward' linkages respectively.
Fit the 3-14 columns of data to models and predict the labels. Save the labels for model_complete within labels_complete and for model_ward within labels_ward.
Compute the rand index using labels_complete and labels_ward.

Task

Compute the rand index to compare the results of using complete and ward linkages. Follow the next steps:

Import functions needed:

rand_score from sklearn.metrics.
AgglomerativeClustering from sklearn.cluster.

Create two models model_complete and model_ward performing a hierarchical clustering with 4 clusters both and 'complete' and 'ward' linkages respectively.
Fit the 3-14 columns of data to models and predict the labels. Save the labels for model_complete within labels_complete and for model_ward within labels_ward.
Compute the rand index using labels_complete and labels_ward.

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

Everything was clear?

Well done! Let's look at the last line charts you built in the previous chapter.

As you can see, only the ward linkage could catch the 'downward up to July' trend. Both results are different. But let's find out how different they are using the rand index.

Task

Compute the rand index to compare the results of using complete and ward linkages. Follow the next steps:

Import functions needed:

rand_score from sklearn.metrics.
AgglomerativeClustering from sklearn.cluster.

Create two models model_complete and model_ward performing a hierarchical clustering with 4 clusters both and 'complete' and 'ward' linkages respectively.
Fit the 3-14 columns of data to models and predict the labels. Save the labels for model_complete within labels_complete and for model_ward within labels_ward.
Compute the rand index using labels_complete and labels_ward.

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

Section 3. Chapter 8

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