Avvistamenti di Pokémon: clustering gerarchico

Continuiamo l’indagine sugli avvistamenti di Pokémon leggendari del precedente esercizio. Ricorda che, nello scatter plot dell’esercizio precedente, hai individuato due aree con alta densità di avvistamenti. Questo suggerisce che i punti si separano in due cluster. In questo esercizio, formerai due cluster degli avvistamenti usando il clustering gerarchico.

'x' e 'y' sono colonne con le coordinate X e Y delle posizioni degli avvistamenti, memorizzate in un DataFrame di pandas, df. Hai a disposizione: matplotlib.pyplot come plt, seaborn come sns e pandas come pd.

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Analisi di cluster in Python

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Istruzioni dell'esercizio

Importa le librerie linkage e fcluster.
Usa la funzione linkage() per calcolare le distanze con il metodo ward.
Genera le etichette di cluster per ogni punto dati in due cluster usando la funzione fcluster().
Traccia i punti con seaborn e assegna un colore diverso a ciascun cluster.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Import linkage and fcluster functions
from scipy.cluster.hierarchy import ____, ____

# Use the linkage() function to compute distance
Z = ____(____, 'ward')

# Generate cluster labels
df['cluster_labels'] = ____(____, ____, criterion='maxclust')

# Plot the points with seaborn
sns.scatterplot(x=____, y=____, hue=____, data=df)
plt.show()

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Analisi di cluster in Python

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Before you are ready to classify news articles, you need to be introduced to the basics of clustering. This chapter familiarizes you with a class of machine learning algorithms called unsupervised learning and then introduces you to clustering, one of the popular unsupervised learning algorithms. You will know about two popular clustering techniques - hierarchical clustering and k-means clustering. The chapter concludes with basic pre-processing steps before you start clustering data.

Exercise 1: Machine Learning non supervisionato: nozioni di base Exercise 2: Unsupervised learning nel mondo reale Exercise 3: Avvistamenti di Pokémon Exercise 4: Basi dell'analisi dei cluster Exercise 5: Avvistamenti di Pokémon: clustering gerarchico

Esercizio in corso

Exercise 6: Avvistamenti di Pokémon: clustering k-means Exercise 7: Preparazione dei dati per l’analisi di clustering Exercise 8: Normalizza semplici dati in lista Exercise 9: Visualizza i dati normalizzati Exercise 10: Normalizzazione di numeri piccoli Exercise 11: FIFA 18: Normalizzare i dati

This chapter focuses on a popular clustering algorithm - hierarchical clustering - and its implementation in SciPy. In addition to the procedure to perform hierarchical clustering, it attempts to help you answer an important question - how many clusters are present in your data? The chapter concludes with a discussion on the limitations of hierarchical clustering and discusses considerations while using hierarchical clustering.

Exercise 1: Basics of hierarchical clustering Exercise 2: Hierarchical clustering: ward method Exercise 3: Hierarchical clustering: single method Exercise 4: Hierarchical clustering: complete method Exercise 5: Visualize clusters Exercise 6: Visualize clusters with matplotlib Exercise 7: Visualize clusters with seaborn Exercise 8: How many clusters?Exercise 9: Create a dendrogram Exercise 10: How many clusters in comic con data?Exercise 11: Limitations of hierarchical clustering Exercise 12: Timing run of hierarchical clustering Exercise 13: FIFA 18: exploring defenders

This chapter introduces a different clustering algorithm - k-means clustering - and its implementation in SciPy. K-means clustering overcomes the biggest drawback of hierarchical clustering that was discussed in the last chapter. As dendrograms are specific to hierarchical clustering, this chapter discusses one method to find the number of clusters before running k-means clustering. The chapter concludes with a discussion on the limitations of k-means clustering and discusses considerations while using this algorithm.

Exercise 1: Basics of k-means clustering Exercise 2: K-means clustering: first exercise Exercise 3: Runtime of k-means clustering Exercise 4: How many clusters?Exercise 5: Elbow method on distinct clusters Exercise 6: Elbow method on uniform data Exercise 7: Limitations of k-means clustering Exercise 8: Impact of seeds on distinct clusters Exercise 9: Uniform clustering patterns Exercise 10: FIFA 18: defenders revisited

Now that you are familiar with two of the most popular clustering techniques, this chapter helps you apply this knowledge to real-world problems. The chapter first discusses the process of finding dominant colors in an image, before moving on to the problem discussed in the introduction - clustering of news articles. The chapter concludes with a discussion on clustering with multiple variables, which makes it difficult to visualize all the data.

Exercise 1: Dominant colors in images Exercise 2: Extract RGB values from image Exercise 3: How many dominant colors?Exercise 4: Display dominant colors Exercise 5: Document clustering Exercise 6: TF-IDF of movie plots Exercise 7: Top terms in movie clusters Exercise 8: Clustering with multiple features Exercise 9: Clustering with many features Exercise 10: Basic checks on clusters Exercise 11: FIFA 18: what makes a complete player?Exercise 12: Farewell!