Term teratas dalam cluster film

Sekarang setelah Anda membuat matriks jarang, buat pusat cluster dan cetak tiga term teratas di setiap cluster. Gunakan metode .todense() untuk mengonversi matriks jarang, tfidf_matrix, menjadi matriks biasa agar fungsi kmeans() dapat memprosesnya. Lalu, gunakan metode .get_feature_names() untuk mendapatkan daftar term dalam objek tfidf_vectorizer. Fungsi zip() di Python menggabungkan dua daftar.

Objek tfidf_vectorizer dan matriks jarang, tfidf_matrix, dari latihan sebelumnya telah dipertahankan di latihan ini. kmeans telah diimpor dari SciPy.

Dengan jumlah titik data yang lebih besar, cluster yang terbentuk akan lebih terdefinisi dengan jelas. Namun, ini memerlukan daya komputasi, sehingga sulit dilakukan dalam latihan di sini.

Latihan ini adalah bagian dari kursus

Analisis Klaster di Python

Petunjuk latihan

Hasilkan pusat cluster melalui fungsi kmeans().
Hasilkan daftar term dari objek tfidf_vectorizer.
Cetak 3 term teratas dari setiap cluster.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

num_clusters = 2

# Generate cluster centers through the kmeans function
cluster_centers, distortion = ____

# Generate terms from the tfidf_vectorizer object
terms = tfidf_vectorizer.____()

for i in range(num_clusters):
    # Sort the terms and print top 3 terms
    center_terms = dict(zip(____, ____))
    sorted_terms = sorted(____, key=center_terms.get, reverse=True)
    print(____)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Analisis Klaster di Python

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

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: Warna dominan pada gambar Exercise 2: Ekstrak nilai RGB dari gambar Exercise 3: Berapa banyak warna dominan?Exercise 4: Tampilkan warna dominan Exercise 5: Pengelompokan dokumen Exercise 6: TF-IDF untuk alur film Exercise 7: Term teratas dalam cluster film

Latihan Saat Ini

Exercise 8: Clustering dengan banyak fitur Exercise 9: Clustering dengan banyak fitur Exercise 10: Pemeriksaan dasar pada klaster Exercise 11: FIFA 18: apa yang membuat pemain komplet?Exercise 12: Sampai jumpa!