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Cluster Analysis in Python
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Course Content

Cluster Analysis in Python

Cluster Analysis in Python

1. K-Means Algorithm
Exploratory Data AnalysisWhat is a K-Means Algorithm?Defining the Number of ClustersCase 1: Three Distinct ClustersCase 2: Four Distinct ClustersClustering Weather DataFebruary vs July Average TemperaturesVisualizing the Dynamics Across Clusters
2. K-Medoids Algorithm
What is a K-Medoids Algorithm?Cluster CentersSilhouette ScoresK-Medoids and the Weather DataMean Yearly Temperatures Across ClustersComparing the Dynamics
3. Hierarchical Clustering
What is a Hierarchical Clustering?DendrogramsSetting Parameters: LinkageRand IndexThe Weather Data and LinkagesDeciding the Number of ClustersWeather Data: Complete and Ward LinkagesHow Similar are the Results?
4. Spectral Clustering
What is a Spectral Clustering?Peculiarity of Spectral ClusteringSetting Parameters: AffinityDefining the Number of ClustersIs 4 the Optimal Number of Clusters?Comparing the Trends Across Clusters

Cluster Centers

In the previous chapter, we compared the K-Means and K-Medoids algorithms. It was mentioned that, unlike K-Means, the K-Medoids algorithm has data points as medoids. Let's visualize the differences.

For example, for the training dataset with three clusters we can visualize the clusters and their centers for both K-Means and K-Medoids. The results are displayed below.

If you watch closely to the right chart and compare it with the central one, you will notice that marked points are data points, unlike the points on the left chart. I've marked these points with green arrows. Let's represent the difference using a more obvious example! The result of clustering using the K-Means algorithm, and cluster centers (labeled with 10) are displayed below.

Task

For the same dataset of points data compute the K-Medoids algorithm, and display the cluster centers. Follow the next steps:

  1. Import KMedoids function from sklearn_extra.cluster library.
  2. Create KMedoids model model with 2 clusters.
  3. Fit the data to the model.
  4. Add 'prediction' column with predicted by model labels to data.
  5. Use the .cluster_centers_ method to extract the clusters centers array.
  6. Visualize the clusters (already done) and centers. Within the second .scatterplot function set hue to 10, and s to 150.

Task

For the same dataset of points data compute the K-Medoids algorithm, and display the cluster centers. Follow the next steps:

  1. Import KMedoids function from sklearn_extra.cluster library.
  2. Create KMedoids model model with 2 clusters.
  3. Fit the data to the model.
  4. Add 'prediction' column with predicted by model labels to data.
  5. Use the .cluster_centers_ method to extract the clusters centers array.
  6. Visualize the clusters (already done) and centers. Within the second .scatterplot function set hue to 10, and s to 150.

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

Everything was clear?

Section 2. Chapter 2
toggle bottom row

Cluster Centers

In the previous chapter, we compared the K-Means and K-Medoids algorithms. It was mentioned that, unlike K-Means, the K-Medoids algorithm has data points as medoids. Let's visualize the differences.

For example, for the training dataset with three clusters we can visualize the clusters and their centers for both K-Means and K-Medoids. The results are displayed below.

If you watch closely to the right chart and compare it with the central one, you will notice that marked points are data points, unlike the points on the left chart. I've marked these points with green arrows. Let's represent the difference using a more obvious example! The result of clustering using the K-Means algorithm, and cluster centers (labeled with 10) are displayed below.

Task

For the same dataset of points data compute the K-Medoids algorithm, and display the cluster centers. Follow the next steps:

  1. Import KMedoids function from sklearn_extra.cluster library.
  2. Create KMedoids model model with 2 clusters.
  3. Fit the data to the model.
  4. Add 'prediction' column with predicted by model labels to data.
  5. Use the .cluster_centers_ method to extract the clusters centers array.
  6. Visualize the clusters (already done) and centers. Within the second .scatterplot function set hue to 10, and s to 150.

Task

For the same dataset of points data compute the K-Medoids algorithm, and display the cluster centers. Follow the next steps:

  1. Import KMedoids function from sklearn_extra.cluster library.
  2. Create KMedoids model model with 2 clusters.
  3. Fit the data to the model.
  4. Add 'prediction' column with predicted by model labels to data.
  5. Use the .cluster_centers_ method to extract the clusters centers array.
  6. Visualize the clusters (already done) and centers. Within the second .scatterplot function set hue to 10, and s to 150.

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

Everything was clear?

Section 2. Chapter 2
toggle bottom row

Cluster Centers

In the previous chapter, we compared the K-Means and K-Medoids algorithms. It was mentioned that, unlike K-Means, the K-Medoids algorithm has data points as medoids. Let's visualize the differences.

For example, for the training dataset with three clusters we can visualize the clusters and their centers for both K-Means and K-Medoids. The results are displayed below.

If you watch closely to the right chart and compare it with the central one, you will notice that marked points are data points, unlike the points on the left chart. I've marked these points with green arrows. Let's represent the difference using a more obvious example! The result of clustering using the K-Means algorithm, and cluster centers (labeled with 10) are displayed below.

Task

For the same dataset of points data compute the K-Medoids algorithm, and display the cluster centers. Follow the next steps:

  1. Import KMedoids function from sklearn_extra.cluster library.
  2. Create KMedoids model model with 2 clusters.
  3. Fit the data to the model.
  4. Add 'prediction' column with predicted by model labels to data.
  5. Use the .cluster_centers_ method to extract the clusters centers array.
  6. Visualize the clusters (already done) and centers. Within the second .scatterplot function set hue to 10, and s to 150.

Task

For the same dataset of points data compute the K-Medoids algorithm, and display the cluster centers. Follow the next steps:

  1. Import KMedoids function from sklearn_extra.cluster library.
  2. Create KMedoids model model with 2 clusters.
  3. Fit the data to the model.
  4. Add 'prediction' column with predicted by model labels to data.
  5. Use the .cluster_centers_ method to extract the clusters centers array.
  6. Visualize the clusters (already done) and centers. Within the second .scatterplot function set hue to 10, and s to 150.

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

Everything was clear?

In the previous chapter, we compared the K-Means and K-Medoids algorithms. It was mentioned that, unlike K-Means, the K-Medoids algorithm has data points as medoids. Let's visualize the differences.

For example, for the training dataset with three clusters we can visualize the clusters and their centers for both K-Means and K-Medoids. The results are displayed below.

If you watch closely to the right chart and compare it with the central one, you will notice that marked points are data points, unlike the points on the left chart. I've marked these points with green arrows. Let's represent the difference using a more obvious example! The result of clustering using the K-Means algorithm, and cluster centers (labeled with 10) are displayed below.

Task

For the same dataset of points data compute the K-Medoids algorithm, and display the cluster centers. Follow the next steps:

  1. Import KMedoids function from sklearn_extra.cluster library.
  2. Create KMedoids model model with 2 clusters.
  3. Fit the data to the model.
  4. Add 'prediction' column with predicted by model labels to data.
  5. Use the .cluster_centers_ method to extract the clusters centers array.
  6. Visualize the clusters (already done) and centers. Within the second .scatterplot function set hue to 10, and s to 150.

Switch to desktop for real-world practiceContinue from where you are using one of the options below
Section 2. Chapter 2
Switch to desktop for real-world practiceContinue from where you are using one of the options below
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