Linkage diverso, clustering gerarchico diverso!

Nel video hai visto un clustering gerarchico dei paesi votanti all'Eurovision Song Contest usando il linkage 'complete'. Ora esegui un clustering gerarchico dei paesi votanti con il linkage 'single' e confronta il dendrogramma risultante con quello del video. Linkage diverso, clustering gerarchico diverso!

Ti viene fornito un array samples. Ogni riga corrisponde a un paese votante e ogni colonna corrisponde a un'esibizione votata. La lista country_names contiene il nome di ciascun paese votante. Questo insieme di dati è stato ottenuto da Eurovision.

Questo esercizio fa parte del corso

Apprendimento non supervisionato in Python

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Istruzioni dell'esercizio

Importa linkage e dendrogram da scipy.cluster.hierarchy.
Esegui il clustering gerarchico su samples usando la funzione linkage() con l'argomento method='single'. Assegna il risultato a mergings.
Traccia un dendrogramma del clustering gerarchico, usando la lista country_names come labels. Inoltre, specifica anche gli argomenti leaf_rotation=90 e leaf_font_size=6, come hai fatto in precedenza.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Perform the necessary imports
import matplotlib.pyplot as plt
from ____ import ____, ____

# Calculate the linkage: mergings
mergings = ____

# Plot the dendrogram
____
plt.show()

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Apprendimento non supervisionato in Python

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Learn how to discover the underlying groups (or "clusters") in a dataset. By the end of this chapter, you'll be clustering companies using their stock market prices, and distinguishing different species by clustering their measurements.

Exercise 1: Unsupervised Learning Exercise 2: How many clusters?Exercise 3: Clustering 2D points Exercise 4: Inspect your clustering Exercise 5: Evaluating a clustering Exercise 6: How many clusters of grain?Exercise 7: Evaluating the grain clustering Exercise 8: Transforming features for better clusterings Exercise 9: Scaling fish data for clustering Exercise 10: Clustering the fish data Exercise 11: Clustering stocks using KMeans Exercise 12: Which stocks move together?

In this chapter, you'll learn about two unsupervised learning techniques for data visualization, hierarchical clustering and t-SNE. Hierarchical clustering merges the data samples into ever-coarser clusters, yielding a tree visualization of the resulting cluster hierarchy. t-SNE maps the data samples into 2d space so that the proximity of the samples to one another can be visualized.

Exercise 1: Visualizzare le gerarchie Exercise 2: Quante fusioni?Exercise 3: Clustering gerarchico dei dati sui cereali Exercise 4: Gerarchie di titoli azionari Exercise 5: Etichette di cluster nel clustering gerarchico Exercise 6: Quali cluster sono più vicini?Exercise 7: Linkage diverso, clustering gerarchico diverso!

Esercizio in corso

Exercise 8: Raggruppamenti intermedi Exercise 9: Estrarre le etichette dei cluster Exercise 10: t-SNE per mappe bidimensionali Exercise 11: Visualizzazione t-SNE dell'insieme di dati sui cereali Exercise 12: Una mappa t-SNE del mercato azionario

Dimension reduction summarizes a dataset using its common occuring patterns. In this chapter, you'll learn about the most fundamental of dimension reduction techniques, "Principal Component Analysis" ("PCA"). PCA is often used before supervised learning to improve model performance and generalization. It can also be useful for unsupervised learning. For example, you'll employ a variant of PCA will allow you to cluster Wikipedia articles by their content!

Exercise 1: Visualizing the PCA transformation Exercise 2: Correlated data in nature Exercise 3: Decorrelating the grain measurements with PCA Exercise 4: Principal components Exercise 5: Intrinsic dimension Exercise 6: The first principal component Exercise 7: Variance of the PCA features Exercise 8: Intrinsic dimension of the fish data Exercise 9: Dimension reduction with PCA Exercise 10: Dimension reduction of the fish measurements Exercise 11: A tf-idf word-frequency array Exercise 12: Clustering Wikipedia part I Exercise 13: Clustering Wikipedia part II

In this chapter, you'll learn about a dimension reduction technique called "Non-negative matrix factorization" ("NMF") that expresses samples as combinations of interpretable parts. For example, it expresses documents as combinations of topics, and images in terms of commonly occurring visual patterns. You'll also learn to use NMF to build recommender systems that can find you similar articles to read, or musical artists that match your listening history!

Exercise 1: Non-negative matrix factorization (NMF)Exercise 2: Non-negative data Exercise 3: NMF applied to Wikipedia articles Exercise 4: NMF features of the Wikipedia articles Exercise 5: NMF reconstructs samples Exercise 6: NMF learns interpretable parts Exercise 7: NMF learns topics of documents Exercise 8: Explore the LED digits dataset Exercise 9: NMF learns the parts of images Exercise 10: PCA doesn't learn parts Exercise 11: Building recommender systems using NMF Exercise 12: Which articles are similar to 'Cristiano Ronaldo'?Exercise 13: Recommend musical artists part I Exercise 14: Recommend musical artists part II Exercise 15: Final thoughts