Tekdüze kümeleme desenleri

Artık tohumların etkisine aşina olduğuna göre, k-means kümelemenin tekdüze kümeler oluşturma yönündeki yanlılığına bakalım.

Sıradaki egzersizde fare benzeri bir veri kümesi kullanacağız. Fare benzeri veri kümesi, bir farenin başına benzeyen nokta gruplarıdır: yüz ve iki kulak için daireler halinde düzenlenmiş üç kümeden oluşur.

Tipik bir fare benzeri veri kümesi şöyle görünür (Kaynak).

Veriler mouse adlı bir pandas DataFrame'inde saklanıyor. x_scaled ve y_scaled, veri noktalarının standartlaştırılmış X ve Y koordinatlarının sütun adlarıdır.

Bu egzersiz

Python ile Kümeleme Analizi

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

SciPy içinde kmeans ve vq fonksiyonlarını içe aktar.
Üç küme ile kmeans() fonksiyonunu kullanarak küme merkezlerini üret.
Yukarıda üretilen küme merkezleriyle vq() kullanarak küme etiketleri oluştur.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# 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()

Kodu Düzenle ve Çalıştır

Bu egzersiz

Python ile Kümeleme Analizi

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

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: k-means kümelemenin temelleri Exercise 2: K-means kümeleme: ilk egzersiz Exercise 3: k-means kümelemenin çalışma süresi Exercise 4: Kaç küme?Exercise 5: Belirgin kümelerde dirsek yöntemi Exercise 6: Uniform verilerde dirsek yöntemi Exercise 7: k-means kümelemenin sınırlamaları Exercise 8: Tohumların farklı kümeler üzerindeki etkisi Exercise 9: Tekdüze kümeleme desenleri

Geçerli Egzersiz

Exercise 10: FIFA 18: savunmacılara yeniden bakalım

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!