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

The Weather Data and Linkages

Let's see how will the dendrograms with different linkage parameter values will look like for the weather data.

Note, that to explore the task's result you will need to scroll over the output block on the right and below.

Tarefa

For the weather data stored in the data variable build the dendrograms for each of 4 types of linkage ('single', 'average', 'complete', and 'ward'. These are already stored in the linkages list). Follow the next steps:

Import the functions needed:

AgglomerativeClustering from sklearn.cluster.
dendrogram and linkage from scipy.cluster.hierarchy.

Iterate over the linkages list using the dummy variable link.
Within the dist variable compute the distances using 3-14 (these are positions, not indices!) columns of data, and passing the method parameter to link.
Build the dendrogram for dist, setting no_labels to True.

Tarefa

Import the functions needed:

AgglomerativeClustering from sklearn.cluster.
dendrogram and linkage from scipy.cluster.hierarchy.

Iterate over the linkages list using the dummy variable link.
Within the dist variable compute the distances using 3-14 (these are positions, not indices!) columns of data, and passing the method parameter to link.
Build the dendrogram for dist, setting no_labels to True.

Note, that you received four dendrograms in the console. To explore them scroll the console down.

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

Tudo estava claro?

Seção 3. Capítulo 5

The Weather Data and Linkages

Let's see how will the dendrograms with different linkage parameter values will look like for the weather data.

Note, that to explore the task's result you will need to scroll over the output block on the right and below.

Tarefa

Import the functions needed:

AgglomerativeClustering from sklearn.cluster.
dendrogram and linkage from scipy.cluster.hierarchy.

Iterate over the linkages list using the dummy variable link.
Within the dist variable compute the distances using 3-14 (these are positions, not indices!) columns of data, and passing the method parameter to link.
Build the dendrogram for dist, setting no_labels to True.

Tarefa

Import the functions needed:

AgglomerativeClustering from sklearn.cluster.
dendrogram and linkage from scipy.cluster.hierarchy.

Iterate over the linkages list using the dummy variable link.
Within the dist variable compute the distances using 3-14 (these are positions, not indices!) columns of data, and passing the method parameter to link.
Build the dendrogram for dist, setting no_labels to True.

Note, that you received four dendrograms in the console. To explore them scroll the console down.

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

Tudo estava claro?

Seção 3. Capítulo 5

The Weather Data and Linkages

Let's see how will the dendrograms with different linkage parameter values will look like for the weather data.

Note, that to explore the task's result you will need to scroll over the output block on the right and below.

Tarefa

Import the functions needed:

AgglomerativeClustering from sklearn.cluster.
dendrogram and linkage from scipy.cluster.hierarchy.

Iterate over the linkages list using the dummy variable link.
Within the dist variable compute the distances using 3-14 (these are positions, not indices!) columns of data, and passing the method parameter to link.
Build the dendrogram for dist, setting no_labels to True.

Tarefa

Import the functions needed:

AgglomerativeClustering from sklearn.cluster.
dendrogram and linkage from scipy.cluster.hierarchy.

Iterate over the linkages list using the dummy variable link.
Within the dist variable compute the distances using 3-14 (these are positions, not indices!) columns of data, and passing the method parameter to link.
Build the dendrogram for dist, setting no_labels to True.

Note, that you received four dendrograms in the console. To explore them scroll the console down.

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

Tudo estava claro?

Let's see how will the dendrograms with different linkage parameter values will look like for the weather data.

Note, that to explore the task's result you will need to scroll over the output block on the right and below.

Tarefa

Import the functions needed:

AgglomerativeClustering from sklearn.cluster.
dendrogram and linkage from scipy.cluster.hierarchy.

Iterate over the linkages list using the dummy variable link.
Within the dist variable compute the distances using 3-14 (these are positions, not indices!) columns of data, and passing the method parameter to link.
Build the dendrogram for dist, setting no_labels to True.

Note, that you received four dendrograms in the console. To explore them scroll the console down.

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

Seção 3. Capítulo 5

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