Book Image

Large Scale Machine Learning with Python

By : Bastiaan Sjardin, Alberto Boschetti
Book Image

Large Scale Machine Learning with Python

By: Bastiaan Sjardin, Alberto Boschetti

Overview of this book

Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy. Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.
Table of Contents (17 chapters)
Large Scale Machine Learning with Python
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface
Index

Clustering – K-means


K-means is an unsupervised algorithm that creates K disjoint clusters of points with equal variance, minimizing the distortion (also named inertia).

Given only one parameter K, representing the number of clusters to be created, the K-means algorithm creates K sets of points S1, S2, …, SK, each of them represented by its centroid: C1, C2, …, CK. The generic centroid, Ci, is simply the mean of the samples of the points associated to the cluster Si in order to minimize the intra-cluster distance. The outputs of the system are as follows:

  1. The composition of the clusters S1, S2, …, SK, that is, the set of points composing the training set that are associated to the cluster number 1, 2, …, K.

  2. The centroids of each cluster, C1, C2, …, CK. Centroids can be used for future associations.

  3. The distortion introduced by the clustering, computed as follows:

This equation denotes the optimization intrinsically done in the K-means algorithm: the centroids are chosen to minimize the intra...