You're reading from Mastering Machine Learning Algorithms Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work

Product type Paperback

Published in Jan 2020

Publisher Packt

ISBN-13 9781838820299

Length 798 pages

Edition 2nd Edition

Languages

Python

Tools

Keras

Concepts

Machine Learning

Authors (2):

Giuseppe Bonaccorso

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Table of Contents (28) Chapters

Preface

1. Machine Learning Model Fundamentals

2. Loss Functions and Regularization FREE CHAPTER

3. Introduction to Semi-Supervised Learning

4. Advanced Semi-Supervised Classification

5. Graph-Based Semi-Supervised Learning

6. Clustering and Unsupervised Models

7. Advanced Clustering and Unsupervised Models

8. Clustering and Unsupervised Models for Marketing

9. Generalized Linear Models and Regression

10. Introduction to Time-Series Analysis

11. Bayesian Networks and Hidden Markov Models

12. The EM Algorithm

13. Component Analysis and Dimensionality Reduction

14. Hebbian Learning

15. Fundamentals of Ensemble Learning

16. Advanced Boosting Algorithms

17. Modeling Neural Networks

18. Optimizing Neural Networks

19. Deep Convolutional Networks

20. Recurrent Neural Networks

21. Autoencoders

22. Introduction to Generative Adversarial Networks

23. Deep Belief Networks

24. Introduction to Reinforcement Learning

25. Advanced Policy Estimation Algorithms

26. Other Books You May Enjoy

27. Index

Advanced Clustering and Unsupervised Models

In this chapter, we will continue to analyze clustering algorithms, focusing our attention on more complex models that can solve problems where K-means fails. These algorithms are extremely helpful in specific contexts (for example, geographical segmentation) where the structure of the data is highly non-linear and any approximation leads to a substantial drop in performance.

In particular, the algorithms and the topics we are going to analyze are:

Fuzzy C-means
Spectral clustering based on the Shi-Malik algorithm
DBSCAN, including the Calinski-Harabasz and Davies-Bouldin scores

The first model is Fuzzy C-means, which is an extension of K-means to a soft-labeling scenario. Just like Generative Gaussian Mixtures, the algorithm helps the data scientist to understand the pseudo-probability (a measure similar to an actual probability) of a data point belonging to all defined clusters.