Book Image

Mastering Machine Learning Algorithms. - Second Edition

By : Giuseppe Bonaccorso
Book Image

Mastering Machine Learning Algorithms. - Second Edition

By: Giuseppe Bonaccorso

Overview of this book

Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios.
Table of Contents (28 chapters)
26
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27
Index

DBSCAN

Most of the clustering methods discussed so far are based on assumptions about the geometrical structure of the dataset. For example, K-means can find the centroids of hyperspherical regions, while spectral clustering has less limitations (in particular, using a KNN affinity matrix), but it requires you to know the desired number of clusters and such a choice conditions the result. On the other hand, spectral clustering, as well as DBSCAN (which stands for Density-Based Spatial Clustering of Applications with Noise), can work with non-convex clusters, while K-means requires such a condition.

DBSCAN is an algorithm proposed by Ester et al. (in Ester M., Kriegel H. P., Sander J., Xu X., A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise, Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, AAAI Press, pp. 226-231, 1996) to overcome all these limitations.

The main assumption is that X represents...