The k-means algorithm is one common approach to clustering. Learn how the algorithm works under the hood, implement k-means clustering in R, visualize and interpret the results, and select the number of clusters when it's not known ahead of time. By the end of the chapter, you'll have applied k-means clustering to a fun "real-world" dataset!

Welcome to the course!

Identify clustering problems

Introduction to k-means clustering

k-means clustering

Results of kmeans()

Visualizing and interpreting results of kmeans()

How k-means works and practical matters

Handling random algorithms

Selecting number of clusters

Introduction to the Pokemon data

Practical matters: working with real data

Review of k-means clustering

Unsupervised learning in R

Hierarchical clustering is another popular method for clustering. The goal of this chapter is to go over how it works, how to use it, and how it compares to k-means clustering.

Pengantar pengelompokan hierarkis

Pengelompokan hierarkis dengan hasil

Memilih jumlah klaster

Menafsirkan dendrogram

Memotong pohon

Keterkaitan clustering dan hal-hal praktis

Metode linkage

Membandingkan metode linkage

Hal praktis: penskalaan

Membandingkan kmeans() dan hclust()

Tinjauan pengelompokan hierarkis

Hierarchical clustering

Principal component analysis, or PCA, is a common approach to dimensionality reduction. Learn exactly what PCA does, visualize the results of PCA with biplots and scree plots, and deal with practical issues such as centering and scaling the data before performing PCA.

Introduction to PCA

PCA using prcomp()

Results of PCA

Additional results of PCA

Visualizing and interpreting PCA results

Interpreting biplots (1)

Interpreting biplots (2)

Variance explained

Visualize variance explained

Practical issues with PCA

Practical issues: scaling

Additional uses of PCA and wrap-up

Dimensionality reduction with PCA

The goal of this chapter is to guide you through a complete analysis using the unsupervised learning techniques covered in the first three chapters. You'll extend what you've learned by combining PCA as a preprocessing step to clustering using data that consist of measurements of cell nuclei of human breast masses.

Introduction to the case study

Preparing the data

Exploratory data analysis

Performing PCA

Interpreting PCA results

PCA review and next steps

Communicating PCA results

Hierarchical clustering of case data

Results of hierarchical clustering

k-means clustering and comparing results

Clustering on PCA results

Wrap-up and review

Putting it all together with a case study

Pokemon data

Wisconsin breast cancer data

Sering kali dalam machine learning, tujuannya adalah menemukan pola dalam data tanpa berusaha membuat prediksi. Ini disebut unsupervised learning. Salah satu penggunaan umum unsupervised learning adalah mengelompokkan konsumen berdasarkan demografi dan riwayat pembelian untuk menjalankan kampanye pemasaran yang terarah. Contoh lainnya adalah menggambarkan faktor-faktor yang tidak terukur yang paling memengaruhi perbedaan tingkat kejahatan antarkota. Kursus ini memberikan pengenalan dasar tentang pengelompokan (clustering) dan pengurangan dimensi di R dari sudut pandang machine learning, sehingga Anda dapat beralih dari data ke wawasan secepat mungkin.


Introduction to R

Kursus ini memperkenalkan klasterisasi dan reduksi dimensi di R dari perspektif pembelajaran mesin.

Unsupervised Learning di R

Kursus ini memberikan pengenalan tentang clustering dan pengurangan dimensi dalam R dari perspektif machine learning.

Associate Data Scientist di R

Dasar-Dasar Pembelajaran Mesin di R

Ilmuwan Pembelajaran Mesin di R

Menafsirkan dendrogram

Unsupervised Learning di R

Latihan interaktif praktis