Book Image

Python Machine Learning

By : Sebastian Raschka
Book Image

Python Machine Learning

By: Sebastian Raschka

Overview of this book

Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world’s leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you’ll soon be able to answer some of the most important questions facing you and your organization.
Table of Contents (21 chapters)
Python Machine Learning
About the Author
About the Reviewers

Locating regions of high density via DBSCAN

Although we can't cover the vast number of different clustering algorithms in this chapter, let's at least introduce one more approach to clustering: Density-based Spatial Clustering of Applications with Noise (DBSCAN). The notion of density in DBSCAN is defined as the number of points within a specified radius .

In DBSCAN, a special label is assigned to each sample (point) using the following criteria:

  • A point is considered as core point if at least a specified number (MinPts) of neighboring points fall within the specified radius
  • A border point is a point that has fewer neighbors than MinPts within , but lies within the radius of a core point
  • All other points that are neither core nor border points are considered as noise points

After labeling the points as core, border, or noise points, the DBSCAN algorithm can be summarized in two simple steps:

  1. Form a separate cluster for each core point or a connected group of core points (core points are...