Pokémon-waarnemingen: hiërarchisch clusteren

We gaan verder met het onderzoek naar de waarnemingen van legendarische Pokémon uit de vorige oefening. Weet je nog dat je in de scatterplot van de vorige oefening twee gebieden zag met veel Pokémon-waarnemingen? Dat betekent dat de punten lijken te splitsen in twee clusters. In deze oefening vorm je twee clusters van de waarnemingen met hiërarchisch clusteren.

'x' en 'y' zijn kolommen met de X- en Y-coördinaten van de locaties van waarnemingen, opgeslagen in een pandas DataFrame, df. De volgende libraries zijn beschikbaar: matplotlib.pyplot als plt, seaborn als sns, en pandas als pd.

Deze oefening maakt deel uit van de cursus

Clusteranalyse in Python

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Oefeninstructies

Importeer de libraries linkage en fcluster.
Gebruik de functie linkage() om afstanden te berekenen met de ward-methode.
Genereer clusterlabels voor elk datapunt met twee clusters met de functie fcluster().
Plot de punten met seaborn en geef elk cluster een andere kleur.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# 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()

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Deze oefening maakt deel uit van de cursus

Clusteranalyse 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: de basis Exercise 2: Unsupervised learning in de echte wereld Exercise 3: Pokémon-waarnemingen Exercise 4: Basis van clusteranalyse Exercise 5: Pokémon-waarnemingen: hiërarchisch clusteren

Huidige oefening

Exercise 6: Pokémon-waarnemingen: k-means clustering Exercise 7: Gegevens voorbereiden voor clusteranalyse Exercise 8: Normaliseer eenvoudige lijstgegevens Exercise 9: Genormaliseerde data visualiseren Exercise 10: Normalisatie van kleine getallen Exercise 11: FIFA 18: Gegevens normaliseren

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!