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Cluster Analysis in Python
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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

Weather Data: Complete and Ward Linkages

The last chart was good, but if you remember the K-Means and K-Medoids algorithms results, you may remember that there was at least one more line that unlike all the others goes downwards close to July. The average linkage in hierarchical clustering didn't catch that dynamic.

We saw that for complete and ward linkages there is sense to consider 4 clusters. Let's find out will they catch that?

Tarefa

Table
  1. Import numpy with np alias.
  2. Iterate over the linkages list. At each step:
  • Create a hierarchical clustering model with 4 clusters and method j named model.
  • Fit the numerical data of temp and predict the labels. Add predicted labels as the 'prediction' column to temp.
  • Create a temp_res DataFrame with monthly averages for each group. To do it group the values of temp by the 'prediction' column, calculate the mean, and then apply the .stack() method.
  • Add column 'method' to temp_res DataFrame with value j being repeated the number of rows in temp_res times.
  • Merge res and temp_res dataframes using .concat function of pd.
  1. Reassign the column names of res to ['Group', 'Month', 'Temp', "Method"].
  2. Within the FacetGrid function set the col parameter to 'Method'. This will build a separate chart for each value of the 'Method' column.
  3. Within the .map function set the seaborn line plot function as the first parameter.

Tarefa

Table
  1. Import numpy with np alias.
  2. Iterate over the linkages list. At each step:
  • Create a hierarchical clustering model with 4 clusters and method j named model.
  • Fit the numerical data of temp and predict the labels. Add predicted labels as the 'prediction' column to temp.
  • Create a temp_res DataFrame with monthly averages for each group. To do it group the values of temp by the 'prediction' column, calculate the mean, and then apply the .stack() method.
  • Add column 'method' to temp_res DataFrame with value j being repeated the number of rows in temp_res times.
  • Merge res and temp_res dataframes using .concat function of pd.
  1. Reassign the column names of res to ['Group', 'Month', 'Temp', "Method"].
  2. Within the FacetGrid function set the col parameter to 'Method'. This will build a separate chart for each value of the 'Method' column.
  3. Within the .map function set the seaborn line plot function as the first parameter.

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Seção 3. Capítulo 7
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Weather Data: Complete and Ward Linkages

The last chart was good, but if you remember the K-Means and K-Medoids algorithms results, you may remember that there was at least one more line that unlike all the others goes downwards close to July. The average linkage in hierarchical clustering didn't catch that dynamic.

We saw that for complete and ward linkages there is sense to consider 4 clusters. Let's find out will they catch that?

Tarefa

Table
  1. Import numpy with np alias.
  2. Iterate over the linkages list. At each step:
  • Create a hierarchical clustering model with 4 clusters and method j named model.
  • Fit the numerical data of temp and predict the labels. Add predicted labels as the 'prediction' column to temp.
  • Create a temp_res DataFrame with monthly averages for each group. To do it group the values of temp by the 'prediction' column, calculate the mean, and then apply the .stack() method.
  • Add column 'method' to temp_res DataFrame with value j being repeated the number of rows in temp_res times.
  • Merge res and temp_res dataframes using .concat function of pd.
  1. Reassign the column names of res to ['Group', 'Month', 'Temp', "Method"].
  2. Within the FacetGrid function set the col parameter to 'Method'. This will build a separate chart for each value of the 'Method' column.
  3. Within the .map function set the seaborn line plot function as the first parameter.

Tarefa

Table
  1. Import numpy with np alias.
  2. Iterate over the linkages list. At each step:
  • Create a hierarchical clustering model with 4 clusters and method j named model.
  • Fit the numerical data of temp and predict the labels. Add predicted labels as the 'prediction' column to temp.
  • Create a temp_res DataFrame with monthly averages for each group. To do it group the values of temp by the 'prediction' column, calculate the mean, and then apply the .stack() method.
  • Add column 'method' to temp_res DataFrame with value j being repeated the number of rows in temp_res times.
  • Merge res and temp_res dataframes using .concat function of pd.
  1. Reassign the column names of res to ['Group', 'Month', 'Temp', "Method"].
  2. Within the FacetGrid function set the col parameter to 'Method'. This will build a separate chart for each value of the 'Method' column.
  3. Within the .map function set the seaborn line plot function as the first parameter.

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo

Tudo estava claro?

Seção 3. Capítulo 7
toggle bottom row

Weather Data: Complete and Ward Linkages

The last chart was good, but if you remember the K-Means and K-Medoids algorithms results, you may remember that there was at least one more line that unlike all the others goes downwards close to July. The average linkage in hierarchical clustering didn't catch that dynamic.

We saw that for complete and ward linkages there is sense to consider 4 clusters. Let's find out will they catch that?

Tarefa

Table
  1. Import numpy with np alias.
  2. Iterate over the linkages list. At each step:
  • Create a hierarchical clustering model with 4 clusters and method j named model.
  • Fit the numerical data of temp and predict the labels. Add predicted labels as the 'prediction' column to temp.
  • Create a temp_res DataFrame with monthly averages for each group. To do it group the values of temp by the 'prediction' column, calculate the mean, and then apply the .stack() method.
  • Add column 'method' to temp_res DataFrame with value j being repeated the number of rows in temp_res times.
  • Merge res and temp_res dataframes using .concat function of pd.
  1. Reassign the column names of res to ['Group', 'Month', 'Temp', "Method"].
  2. Within the FacetGrid function set the col parameter to 'Method'. This will build a separate chart for each value of the 'Method' column.
  3. Within the .map function set the seaborn line plot function as the first parameter.

Tarefa

Table
  1. Import numpy with np alias.
  2. Iterate over the linkages list. At each step:
  • Create a hierarchical clustering model with 4 clusters and method j named model.
  • Fit the numerical data of temp and predict the labels. Add predicted labels as the 'prediction' column to temp.
  • Create a temp_res DataFrame with monthly averages for each group. To do it group the values of temp by the 'prediction' column, calculate the mean, and then apply the .stack() method.
  • Add column 'method' to temp_res DataFrame with value j being repeated the number of rows in temp_res times.
  • Merge res and temp_res dataframes using .concat function of pd.
  1. Reassign the column names of res to ['Group', 'Month', 'Temp', "Method"].
  2. Within the FacetGrid function set the col parameter to 'Method'. This will build a separate chart for each value of the 'Method' column.
  3. Within the .map function set the seaborn line plot function as the first parameter.

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo

Tudo estava claro?

The last chart was good, but if you remember the K-Means and K-Medoids algorithms results, you may remember that there was at least one more line that unlike all the others goes downwards close to July. The average linkage in hierarchical clustering didn't catch that dynamic.

We saw that for complete and ward linkages there is sense to consider 4 clusters. Let's find out will they catch that?

Tarefa

Table
  1. Import numpy with np alias.
  2. Iterate over the linkages list. At each step:
  • Create a hierarchical clustering model with 4 clusters and method j named model.
  • Fit the numerical data of temp and predict the labels. Add predicted labels as the 'prediction' column to temp.
  • Create a temp_res DataFrame with monthly averages for each group. To do it group the values of temp by the 'prediction' column, calculate the mean, and then apply the .stack() method.
  • Add column 'method' to temp_res DataFrame with value j being repeated the number of rows in temp_res times.
  • Merge res and temp_res dataframes using .concat function of pd.
  1. Reassign the column names of res to ['Group', 'Month', 'Temp', "Method"].
  2. Within the FacetGrid function set the col parameter to 'Method'. This will build a separate chart for each value of the 'Method' column.
  3. Within the .map function set the seaborn line plot function as the first parameter.

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Seção 3. Capítulo 7
Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
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