You're reading from Python Machine Learning by Example Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn

Product type Paperback

Published in Oct 2020

Publisher Packt

ISBN-13 9781800209718

Length 526 pages

Edition 3rd Edition

Languages

Python

Tools

PyTorch

Concepts

Machine Learning

Author (1):

Yuxi (Hayden) Liu

View More author details

Table of Contents (17) Chapters

Preface

1. Getting Started with Machine Learning and Python

2. Building a Movie Recommendation Engine with Naïve Bayes FREE CHAPTER

3. Recognizing Faces with Support Vector Machine

4. Predicting Online Ad Click-Through with Tree-Based Algorithms

5. Predicting Online Ad Click-Through with Logistic Regression

6. Scaling Up Prediction to Terabyte Click Logs

7. Predicting Stock Prices with Regression Algorithms

8. Predicting Stock Prices with Artificial Neural Networks

9. Mining the 20 Newsgroups Dataset with Text Analysis Techniques

10. Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling

11. Machine Learning Best Practices

12. Categorizing Images of Clothing with Convolutional Neural Networks

13. Making Predictions with Sequences Using Recurrent Neural Networks

14. Making Decisions in Complex Environments with Reinforcement Learning

15. Other Books You May Enjoy

16. Index

Clustering newsgroups data using k-means

The newsgroups data comes with labels, which are the categories of the newsgroups, and a number of categories that are closely related or even overlapping, for instance, the five computer groups: comp.graphics, comp.os.ms-windows.misc, comp.sys.ibm.pc.hardware, comp.sys.mac.hardware, and comp.windows.x, and the two religion-related ones: alt.atheism and talk.religion.misc.

Let's now pretend we don't know those labels or they don't exist. Will samples from related topics be clustered together? We will now resort to the k-means clustering algorithm.

How does k-means clustering work?

The goal of the k-means algorithm is to partition the data into k groups based on feature similarities. K is a predefined property of a k-means clustering model. Each of the k clusters is specified by a centroid (center of a cluster) and each data sample belongs to the cluster with the nearest centroid. During training, the algorithm...