Book contents
- Frontmatter
- Dedication
- Contents
- Preface
- Notation
- Part One Machine Learning
- Executive Summary
- 1 Rudiments of Statistical Learning Theory
- 2 Vapnik–Chervonenkis Dimension
- 3 Learnability for Binary Classification
- 4 Support Vector Machines
- 5 Reproducing Kernel Hilbert Spaces
- 6 Regression and Regularization
- 7 Clustering
- 8 Dimension Reduction
- Part Two Optimal Recovery
- Part Three Compressive Sensing
- Part Four Optimization
- Part Five Neural Networks
- Appendices
- References
- Index
7 - Clustering
from Part One - Machine Learning
Published online by Cambridge University Press: 21 April 2022
- Frontmatter
- Dedication
- Contents
- Preface
- Notation
- Part One Machine Learning
- Executive Summary
- 1 Rudiments of Statistical Learning Theory
- 2 Vapnik–Chervonenkis Dimension
- 3 Learnability for Binary Classification
- 4 Support Vector Machines
- 5 Reproducing Kernel Hilbert Spaces
- 6 Regression and Regularization
- 7 Clustering
- 8 Dimension Reduction
- Part Two Optimal Recovery
- Part Three Compressive Sensing
- Part Four Optimization
- Part Five Neural Networks
- Appendices
- References
- Index
Summary
This chapter considers the unsupervised learning task known as clustering, which consists in grouping unlabeled datapoints based on some similarity information. The single-linkage algorithm is examined first. Then, the Lloyd algorithm is presented to illustrate the center-based clustering strategy. Finally, the problem of detecting two communities via spectral clustering is analyzed under the stochastic block model.
- Type
- Chapter
- Information
- Mathematical Pictures at a Data Science Exhibition , pp. 47 - 55Publisher: Cambridge University PressPrint publication year: 2022