Clustering gerarchico: metodo ward

È tempo di Comic-Con! Comic-Con è una convention annuale dedicata ai fumetti che si tiene nelle principali città del mondo. Hai i dati dell'affluenza dell'anno scorso, cioè il numero di persone presenti nell'area della convention in un dato momento. Vuoi decidere dove posizionare il tuo stand per massimizzare le vendite. Usando il metodo ward, applica il clustering gerarchico per trovare i due punti di attrazione nell'area.

I dati sono in un DataFrame di pandas, comic_con. x_scaled e y_scaled sono i nomi delle colonne con le coordinate X e Y standardizzate delle persone in un dato momento.

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Analisi di cluster in Python

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Istruzioni dell'esercizio

Importa fcluster e linkage da scipy.cluster.hierarchy.
Usa il metodo ward nella funzione linkage().
Assegna le etichette di cluster formando 2 cluster piatti da distance_matrix.
Esegui il codice di plotting per vedere i risultati.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

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

# Use the linkage() function
distance_matrix = ____(comic_con[['x_scaled', 'y_scaled']], ____ = ____, metric = 'euclidean')

# Assign cluster labels
comic_con['cluster_labels'] = ____(____, ____, criterion='maxclust')

# Plot clusters
sns.scatterplot(x='x_scaled', y='y_scaled', 
                hue='cluster_labels', data = comic_con)
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: Unsupervised learning: basics Exercise 2: Unsupervised learning in real world Exercise 3: Pokémon sightings Exercise 4: Basics of cluster analysis Exercise 5: Pokémon sightings: hierarchical clustering Exercise 6: Pokémon sightings: k-means clustering Exercise 7: Data preparation for cluster analysis Exercise 8: Normalize basic list data Exercise 9: Visualize normalized data Exercise 10: Normalization of small numbers Exercise 11: FIFA 18: Normalize data

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: Basi del clustering gerarchico Exercise 2: Clustering gerarchico: metodo ward

Esercizio in corso

Exercise 3: Clustering gerarchico: metodo single Exercise 4: Clustering gerarchico: metodo complete Exercise 5: Visualizza i cluster Exercise 6: Visualizzare i cluster con matplotlib Exercise 7: Visualizza i cluster con seaborn Exercise 8: Quanti cluster?Exercise 9: Crea un dendrogramma Exercise 10: Quanti cluster nei dati del comic con?Exercise 11: Limiti del clustering gerarchico Exercise 12: Misurare i tempi del clustering gerarchico Exercise 13: FIFA 18: esplorare i difensori

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!