Clusters visualiseren met seaborn

Laten we nu de footfall-gegevensset van Comic Con visualiseren met de module seaborn. Clusters visualiseren met seaborn is eenvoudiger dankzij de ingebouwde hue-functie voor clusterlabels.

De gegevens staan in een pandas DataFrame, comic_con. x_scaled en y_scaled zijn de kolomnamen van de gestandaardiseerde X- en Y-coördinaten van mensen op een bepaald moment. cluster_labels bevat de clusterlabels. Een linkage-object is opgeslagen in de variabele distance_matrix.

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Clusteranalyse in Python

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Importeer de module seaborn als sns.
Maak een scatterplot met de methode .scatterplot() van seaborn, met de clusterlabels als het hue-argument.

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# Import the seaborn module
____

# Plot a scatter plot using seaborn
sns.scatterplot(x=____, 
                y=____, 
                ____=____, 
                data = comic_con)
plt.show()

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Deze oefening maakt deel uit van de cursus

Clusteranalyse in Python

SkillTag.level.intermediateSkillTag.label

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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: Basis van hiërarchisch clusteren Exercise 2: Hiërarchische clustering: ward-methode Exercise 3: Hiërarchische clustering: single-methode Exercise 4: Hiërarchische clustering: complete-methode Exercise 5: Clusters visualiseren Exercise 6: Clusters visualiseren met matplotlib Exercise 7: Clusters visualiseren met seaborn

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Exercise 8: Hoeveel clusters?Exercise 9: Maak een dendrogram Exercise 10: Hoeveel clusters in comic con-gegevens?Exercise 11: Beperkingen van hiërarchisch clusteren Exercise 12: Tijdmeting van hiërarchisch clusteren Exercise 13: FIFA 18: verdedigers verkennen

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!