Applied Unsupervised Learning with R
Applied Unsupervised Learning with R:
Uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA
by Alok Malik
English | 2019 | ISBN: 1789956390 | 315 Pages | PDF conv | 20.19 MB
Uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA
by Alok Malik
English | 2019 | ISBN: 1789956390 | 315 Pages | PDF conv | 20.19 MB
This book begins with the most important and commonly used method for unsupervised learning - clustering - and explains the three main clustering algorithms - k-means, divisive, and agglomerative. Following this, you'll study market basket analysis, kernel density estimation, principal component analysis, and anomaly detection. You'll be introduced to these methods using code written in R, with further instructions on how to work with, edit, and improve R code. To help you gain a practical understanding, the book also features useful tips on applying these methods to real business problems, including market segmentation and fraud detection. By working through interesting activities, you'll explore data encoders and latent variable models.