Pola pengelompokan seragam

Sekarang setelah Anda memahami dampak seed, mari kita lihat bias pada k-means clustering yang cenderung membentuk klaster yang seragam.

Mari gunakan himpunan data berbentuk tikus untuk latihan berikutnya. Himpunan data berbentuk tikus adalah sekumpulan titik yang menyerupai kepala tikus: terdapat tiga klaster titik yang tersusun melingkar, masing-masing untuk wajah dan dua telinga tikus.

Berikut tampilan tipikal himpunan data berbentuk tikus (Sumber).

Data disimpan dalam pandas DataFrame, mouse. x_scaled dan y_scaled adalah nama kolom untuk koordinat X dan Y yang telah distandardisasi dari titik data.

Latihan ini adalah bagian dari kursus

Analisis Klaster di Python

Petunjuk latihan

Impor fungsi kmeans dan vq di SciPy.
Hasilkan pusat klaster menggunakan fungsi kmeans() dengan tiga klaster.
Buat label klaster dengan vq() menggunakan pusat klaster yang dihasilkan di atas.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Import the kmeans and vq functions
____

# Generate cluster centers
cluster_centers, distortion = ____

# Assign cluster labels
mouse['cluster_labels'], distortion_list = ____

# Plot clusters
sns.scatterplot(x='x_scaled', y='y_scaled', 
                hue='cluster_labels', data = mouse)
plt.show()

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: Dasar-dasar pengelompokan k-means Exercise 2: K-means clustering: latihan pertama Exercise 3: Waktu eksekusi k-means clustering Exercise 4: Berapa banyak klaster?Exercise 5: Metode elbow pada klaster yang berbeda jelas Exercise 6: Metode elbow pada data seragam Exercise 7: Keterbatasan pengelompokan k-means Exercise 8: Dampak seed pada klaster yang berbeda Exercise 9: Pola pengelompokan seragam

Latihan Saat Ini

Exercise 10: FIFA 18: kembali ke defender

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!