TF-IDF von Filmhandlungen

Lass uns die Handlungen zufällig ausgewählter Filme für das Clustering von Dokumenten verwenden. Bevor du Dokumente clustern kannst, müssen sie von unerwünschtem Rauschen (z. B. Sonderzeichen und Stoppwörtern) bereinigt und über TF-IDF der Dokumente in eine Sparse-Matrix umgewandelt werden.

Verwende die Klasse TfidfVectorizer, um das TF-IDF der in der Liste plots gespeicherten Filmhandlungen zu berechnen. Die Funktion remove_noise() steht als tokenizer in der Klasse TfidfVectorizer zur Verfügung. Die Methode .fit_transform() passt die Daten an die TfidfVectorizer-Objekte an und erzeugt anschließend die TF-IDF-Sparse-Matrix.

Hinweis: Das Ausführen der Methode .fit_transform() dauert ein paar Sekunden.

Diese Übung ist Teil des Kurses

<Kurs>Cluster Analysis in Python</Kurs>

Übungsanweisungen

Importiere die Klasse TfidfVectorizer aus sklearn.
Initialisiere die Klasse TfidfVectorizer mit minimalen und maximalen Frequenzen von 0.1 und 0.75 sowie 50 maximalen Features.
Verwende die Methode fit_transform() auf der initialisierten Klasse TfidfVectorizer mit der Liste plots.

Interaktive praktische Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

# Import TfidfVectorizer class from sklearn
from sklearn.feature_extraction.text import ____

# Initialize TfidfVectorizer
tfidf_vectorizer = TfidfVectorizer(____)

# Use the .fit_transform() method on the list plots
tfidf_matrix = ____

Code bearbeiten und ausführen

Diese Übung ist Teil des Kurses

<Kurs>Cluster Analysis in Python</Kurs>

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4.8+

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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: 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: Dominante Farben in Bildern Exercise 2: RGB-Werte aus einem Bild extrahieren Exercise 3: Wie viele dominierende Farben?Exercise 4: Dominante Farben anzeigen Exercise 5: Dokumenten-Clustering Exercise 6: TF-IDF von Filmhandlungen

Aktuelle Übung

Exercise 7: Top-Terme in Film-Clustern Exercise 8: Clustering mit mehreren Merkmalen Exercise 9: Clustering mit vielen Merkmalen Exercise 10: Grundlegende Checks für Cluster Exercise 11: FIFA 18: Was macht einen kompletten Spieler aus?Exercise 12: Zum Abschied!