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Machine Learning Algorithms

You're reading from Machine Learning Algorithms A reference guide to popular algorithms for data science and machine learning

Product type Paperback

Published in Jul 2017

Publisher Packt

ISBN-13 9781785889622

Length 360 pages

Edition 1st Edition

Languages

Python

Tools

Processing

Concepts

Data Science

Author (1):

Giuseppe Bonaccorso

View More author details

Table of Contents (22) Chapters

Title Page

Credits

About the Author

About the Reviewers

www.PacktPub.com

Customer Feedback

Preface

1. A Gentle Introduction to Machine Learning

2. Important Elements in Machine Learning FREE CHAPTER

3. Feature Selection and Feature Engineering

4. Linear Regression

5. Logistic Regression

6. Naive Bayes

7. Support Vector Machines

8. Decision Trees and Ensemble Learning

9. Clustering Fundamentals

10. Hierarchical Clustering

11. Introduction to Recommendation Systems

12. Introduction to Natural Language Processing

13. Topic Modeling and Sentiment Analysis in NLP

14. A Brief Introduction to Deep Learning and TensorFlow

15. Creating a Machine Learning Architecture

Chapter 9. Clustering Fundamentals

In this chapter, we're going to introduce the basic concepts of clustering and the structure of k-means, a quite common algorithm that can solve many problems efficiently. However, its assumptions are very strong, in particular those concerning the convexity of the clusters, and this can lead to some limitations in its adoption. We're going to discuss its mathematical foundation and how it can be optimized. Moreover, we're going to analyze two alternatives that can be employed when k-means fails to cluster a dataset. These alternatives are DBSCAN, (which works by considering the differences of sample density), and spectral clustering, a very powerful approach based on the affinity among points.

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