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

Peculiarity of Spectral Clustering

The result of the last chapter was great! Spectral clustering correctly figured out the structure of the clusters, unlike K-Means and K-Medoids algorithms.

Thus, spectral clustering is very useful in case of intersect/overlapping clusters or when you can not use mean points and the centers.

For example, let's explore such a case. Given the 2-D training set of points, the scatter plot for which is built below.

Seems like 4 circles, therefore 4 clusters, doesn't it? But that is what K-Means will show us.

Not what we expected to see. Let's see how will spectral clustering deal with this data.

Please note, that the spectral clustering algorithm may take a long time to perform since it is based on hard math.

Tarefa

For the given set of 2-D points data perform a spectral clustering. Follow the next steps:

Import SpectralClustering function from sklearn.cluster.
Create a SpectralClustering model with 4 clusters.
Fit the data and predict the labels. Save predicted labels within the 'prediction' column of data.
Build scatter plot with 'x' column on the x-axis 'y' column on the y-axis for each value of 'prediction' (separate color for each value). Do not forget to display the plot.

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Seção 4. Capítulo 2

Peculiarity of Spectral Clustering

The result of the last chapter was great! Spectral clustering correctly figured out the structure of the clusters, unlike K-Means and K-Medoids algorithms.

Thus, spectral clustering is very useful in case of intersect/overlapping clusters or when you can not use mean points and the centers.

For example, let's explore such a case. Given the 2-D training set of points, the scatter plot for which is built below.

Seems like 4 circles, therefore 4 clusters, doesn't it? But that is what K-Means will show us.

Not what we expected to see. Let's see how will spectral clustering deal with this data.

Please note, that the spectral clustering algorithm may take a long time to perform since it is based on hard math.

Tarefa

For the given set of 2-D points data perform a spectral clustering. Follow the next steps:

Import SpectralClustering function from sklearn.cluster.
Create a SpectralClustering model with 4 clusters.
Fit the data and predict the labels. Save predicted labels within the 'prediction' column of data.
Build scatter plot with 'x' column on the x-axis 'y' column on the y-axis for each value of 'prediction' (separate color for each value). Do not forget to display the plot.

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Seção 4. Capítulo 2

Peculiarity of Spectral Clustering

The result of the last chapter was great! Spectral clustering correctly figured out the structure of the clusters, unlike K-Means and K-Medoids algorithms.

Thus, spectral clustering is very useful in case of intersect/overlapping clusters or when you can not use mean points and the centers.

For example, let's explore such a case. Given the 2-D training set of points, the scatter plot for which is built below.

Seems like 4 circles, therefore 4 clusters, doesn't it? But that is what K-Means will show us.

Not what we expected to see. Let's see how will spectral clustering deal with this data.

Please note, that the spectral clustering algorithm may take a long time to perform since it is based on hard math.

Tarefa

For the given set of 2-D points data perform a spectral clustering. Follow the next steps:

Import SpectralClustering function from sklearn.cluster.
Create a SpectralClustering model with 4 clusters.
Fit the data and predict the labels. Save predicted labels within the 'prediction' column of data.
Build scatter plot with 'x' column on the x-axis 'y' column on the y-axis for each value of 'prediction' (separate color for each value). Do not forget 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?

Obrigado pelo seu feedback!

The result of the last chapter was great! Spectral clustering correctly figured out the structure of the clusters, unlike K-Means and K-Medoids algorithms.

Thus, spectral clustering is very useful in case of intersect/overlapping clusters or when you can not use mean points and the centers.

For example, let's explore such a case. Given the 2-D training set of points, the scatter plot for which is built below.

Seems like 4 circles, therefore 4 clusters, doesn't it? But that is what K-Means will show us.

Not what we expected to see. Let's see how will spectral clustering deal with this data.

Please note, that the spectral clustering algorithm may take a long time to perform since it is based on hard math.

Tarefa

For the given set of 2-D points data perform a spectral clustering. Follow the next steps:

Import SpectralClustering function from sklearn.cluster.
Create a SpectralClustering model with 4 clusters.
Fit the data and predict the labels. Save predicted labels within the 'prediction' column of data.
Build scatter plot with 'x' column on the x-axis 'y' column on the y-axis for each value of 'prediction' (separate color for each value). Do not forget to display the plot.

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Seção 4. Capítulo 2

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