Conteúdo do Curso

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

What is a K-Medoids Algorithm?

You did well in the first section! Now it's time to learn a new algorithm!

So, unlike centroid, medoid has to be a data point - which is the key difference. In the case of large and 'grouped' data there most likely will be an insignificant difference.

The K-Medoids algorithm works similarly to the K-Means, but in the first step, the random points have to be data points. These are medoids. Then, each data point is associated with the closest medoid by some metric. Then, the medoids swap with some data points, and the cost function (sum of the variances for all the points within a cluster) compares with the one in the previous step until we get the minimum possible values.

How to implement the K-Medoids algorithm using Python? The same way you did with the K-Means algorithm! The key function there is KMedoids from the sklearn_extra.cluster library. The algorithm is the next:

Create a KMedoids model assigned to a certain variable.
Compute K-Medoids clustering using the .fit() method of KMedoids object with data set as a parameter.
Predict the labels using the fitted model by applying the .predict() function to the KMedoids object with the data set as a parameter.
(not necessary) Visualize the result of clustering.

For example, let's try to cluster the points using the K-Medoids method. The scatter plot for the data points is below.

Tarefa

Implement the K-Medoids algorithm to divide the points into two groups. To do it, follow the next steps:

Import KMedoids from sklearn_extra.cluster.
Create a KMedoids model object with 2 clusters assigned to the model variable.
Fit the data to the model.
Predict the labels for data using model. Save the labels within the prediction variable.
Add 'prediction' column to data filled with prediction values.
Build a scatter plot with 'x' column on the x-axis, 'y' column on the y-axis, and a 'prediction' column as the color of the point. Do not forget to apply the respective function of plt to display the plot.

Tarefa

Implement the K-Medoids algorithm to divide the points into two groups. To do it, follow the next steps:

Import KMedoids from sklearn_extra.cluster.
Create a KMedoids model object with 2 clusters assigned to the model variable.
Fit the data to the model.
Predict the labels for data using model. Save the labels within the prediction variable.
Add 'prediction' column to data filled with prediction values.
Build a scatter plot with 'x' column on the x-axis, 'y' column on the y-axis, and a 'prediction' column as the color of the point. Do not forget to apply the respective function of plt to display the plot.

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Tudo estava claro?

Seção 2. Capítulo 1

What is a K-Medoids Algorithm?

You did well in the first section! Now it's time to learn a new algorithm!

So, unlike centroid, medoid has to be a data point - which is the key difference. In the case of large and 'grouped' data there most likely will be an insignificant difference.

Create a KMedoids model assigned to a certain variable.
Compute K-Medoids clustering using the .fit() method of KMedoids object with data set as a parameter.
Predict the labels using the fitted model by applying the .predict() function to the KMedoids object with the data set as a parameter.
(not necessary) Visualize the result of clustering.

For example, let's try to cluster the points using the K-Medoids method. The scatter plot for the data points is below.

Tarefa

Implement the K-Medoids algorithm to divide the points into two groups. To do it, follow the next steps:

Import KMedoids from sklearn_extra.cluster.
Create a KMedoids model object with 2 clusters assigned to the model variable.
Fit the data to the model.
Predict the labels for data using model. Save the labels within the prediction variable.
Add 'prediction' column to data filled with prediction values.
Build a scatter plot with 'x' column on the x-axis, 'y' column on the y-axis, and a 'prediction' column as the color of the point. Do not forget to apply the respective function of plt to display the plot.

Tarefa

Implement the K-Medoids algorithm to divide the points into two groups. To do it, follow the next steps:

Import KMedoids from sklearn_extra.cluster.
Create a KMedoids model object with 2 clusters assigned to the model variable.
Fit the data to the model.
Predict the labels for data using model. Save the labels within the prediction variable.
Add 'prediction' column to data filled with prediction values.
Build a scatter plot with 'x' column on the x-axis, 'y' column on the y-axis, and a 'prediction' column as the color of the point. Do not forget to apply the respective function of plt to display the plot.

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

Tudo estava claro?

Seção 2. Capítulo 1

What is a K-Medoids Algorithm?

You did well in the first section! Now it's time to learn a new algorithm!

So, unlike centroid, medoid has to be a data point - which is the key difference. In the case of large and 'grouped' data there most likely will be an insignificant difference.

Create a KMedoids model assigned to a certain variable.
Compute K-Medoids clustering using the .fit() method of KMedoids object with data set as a parameter.
Predict the labels using the fitted model by applying the .predict() function to the KMedoids object with the data set as a parameter.
(not necessary) Visualize the result of clustering.

For example, let's try to cluster the points using the K-Medoids method. The scatter plot for the data points is below.

Tarefa

Implement the K-Medoids algorithm to divide the points into two groups. To do it, follow the next steps:

Import KMedoids from sklearn_extra.cluster.
Create a KMedoids model object with 2 clusters assigned to the model variable.
Fit the data to the model.
Predict the labels for data using model. Save the labels within the prediction variable.
Add 'prediction' column to data filled with prediction values.
Build a scatter plot with 'x' column on the x-axis, 'y' column on the y-axis, and a 'prediction' column as the color of the point. Do not forget to apply the respective function of plt to display the plot.

Tarefa

Implement the K-Medoids algorithm to divide the points into two groups. To do it, follow the next steps:

Import KMedoids from sklearn_extra.cluster.
Create a KMedoids model object with 2 clusters assigned to the model variable.
Fit the data to the model.
Predict the labels for data using model. Save the labels within the prediction variable.
Add 'prediction' column to data filled with prediction values.
Build a scatter plot with 'x' column on the x-axis, 'y' column on the y-axis, and a 'prediction' column as the color of the point. Do not forget to apply the respective function of plt to display the plot.

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

Tudo estava claro?

You did well in the first section! Now it's time to learn a new algorithm!

So, unlike centroid, medoid has to be a data point - which is the key difference. In the case of large and 'grouped' data there most likely will be an insignificant difference.

Create a KMedoids model assigned to a certain variable.
Compute K-Medoids clustering using the .fit() method of KMedoids object with data set as a parameter.
Predict the labels using the fitted model by applying the .predict() function to the KMedoids object with the data set as a parameter.
(not necessary) Visualize the result of clustering.

For example, let's try to cluster the points using the K-Medoids method. The scatter plot for the data points is below.

Tarefa

Implement the K-Medoids algorithm to divide the points into two groups. To do it, follow the next steps:

Import KMedoids from sklearn_extra.cluster.
Create a KMedoids model object with 2 clusters assigned to the model variable.
Fit the data to the model.
Predict the labels for data using model. Save the labels within the prediction variable.
Add 'prediction' column to data filled with prediction values.
Build a scatter plot with 'x' column on the x-axis, 'y' column on the y-axis, and a 'prediction' column as the color of the point. Do not forget to apply the respective function of plt to display the plot.

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

Seção 2. Capítulo 1

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