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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

bookComparing the Dynamics

That's an interesting result! The yearly average temperatures across clusters significantly differ for 3 of them (47.3, 60.9, and 79.24). It seems like a good split.

Now let's visualize the monthly dynamics of average temperatures across clusters, and compare the result with the 5 clusters by the K-Means algorithm. The respective line plot is below.

Task

Visualize the monthly temperature dynamics across clusters. Follow the next steps:

  1. Import KMedoids function from sklearn_extra.cluster.
  2. Create a KMedoids object named model with 4 clusters.
  3. Fit the 3-15 columns (these are not indices, but positions) of data to model.
  4. Add the 'prediction' column to data with predicted by model labels.
  5. Calculate the monthly averages using data and save the result within the d DataFrame:
  • Group the observations by the 'prediction' column.
  • Calculate the mean values.
  • Stack the columns into indices (already done).
  • Reset the indices.
  1. Assign ['Group', 'Month', 'Temp'] as columns names of d.
  2. Build lineplot with 'Month' on the x-axis, 'Temp' on the y-axis for each 'Group' of d DataFrame (i.e. separate line and color for each 'Group').

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Section 2. Chapter 6
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bookComparing the Dynamics

That's an interesting result! The yearly average temperatures across clusters significantly differ for 3 of them (47.3, 60.9, and 79.24). It seems like a good split.

Now let's visualize the monthly dynamics of average temperatures across clusters, and compare the result with the 5 clusters by the K-Means algorithm. The respective line plot is below.

Task

Visualize the monthly temperature dynamics across clusters. Follow the next steps:

  1. Import KMedoids function from sklearn_extra.cluster.
  2. Create a KMedoids object named model with 4 clusters.
  3. Fit the 3-15 columns (these are not indices, but positions) of data to model.
  4. Add the 'prediction' column to data with predicted by model labels.
  5. Calculate the monthly averages using data and save the result within the d DataFrame:
  • Group the observations by the 'prediction' column.
  • Calculate the mean values.
  • Stack the columns into indices (already done).
  • Reset the indices.
  1. Assign ['Group', 'Month', 'Temp'] as columns names of d.
  2. Build lineplot with 'Month' on the x-axis, 'Temp' on the y-axis for each 'Group' of d DataFrame (i.e. separate line and color for each 'Group').

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 2. Chapter 6
toggle bottom row

bookComparing the Dynamics

That's an interesting result! The yearly average temperatures across clusters significantly differ for 3 of them (47.3, 60.9, and 79.24). It seems like a good split.

Now let's visualize the monthly dynamics of average temperatures across clusters, and compare the result with the 5 clusters by the K-Means algorithm. The respective line plot is below.

Task

Visualize the monthly temperature dynamics across clusters. Follow the next steps:

  1. Import KMedoids function from sklearn_extra.cluster.
  2. Create a KMedoids object named model with 4 clusters.
  3. Fit the 3-15 columns (these are not indices, but positions) of data to model.
  4. Add the 'prediction' column to data with predicted by model labels.
  5. Calculate the monthly averages using data and save the result within the d DataFrame:
  • Group the observations by the 'prediction' column.
  • Calculate the mean values.
  • Stack the columns into indices (already done).
  • Reset the indices.
  1. Assign ['Group', 'Month', 'Temp'] as columns names of d.
  2. Build lineplot with 'Month' on the x-axis, 'Temp' on the y-axis for each 'Group' of d DataFrame (i.e. separate line and color for each 'Group').

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

That's an interesting result! The yearly average temperatures across clusters significantly differ for 3 of them (47.3, 60.9, and 79.24). It seems like a good split.

Now let's visualize the monthly dynamics of average temperatures across clusters, and compare the result with the 5 clusters by the K-Means algorithm. The respective line plot is below.

Task

Visualize the monthly temperature dynamics across clusters. Follow the next steps:

  1. Import KMedoids function from sklearn_extra.cluster.
  2. Create a KMedoids object named model with 4 clusters.
  3. Fit the 3-15 columns (these are not indices, but positions) of data to model.
  4. Add the 'prediction' column to data with predicted by model labels.
  5. Calculate the monthly averages using data and save the result within the d DataFrame:
  • Group the observations by the 'prediction' column.
  • Calculate the mean values.
  • Stack the columns into indices (already done).
  • Reset the indices.
  1. Assign ['Group', 'Month', 'Temp'] as columns names of d.
  2. Build lineplot with 'Month' on the x-axis, 'Temp' on the y-axis for each 'Group' of d DataFrame (i.e. separate line and color for each 'Group').

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 2. Chapter 6
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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