Book Image

Apache Spark 2.x Machine Learning Cookbook

By : Mohammed Guller, Siamak Amirghodsi, Shuen Mei, Meenakshi Rajendran, Broderick Hall
Book Image

Apache Spark 2.x Machine Learning Cookbook

By: Mohammed Guller, Siamak Amirghodsi, Shuen Mei, Meenakshi Rajendran, Broderick Hall

Overview of this book

Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability, and optimization. Learning about algorithms enables a wide range of applications, from everyday tasks such as product recommendations and spam filtering to cutting edge applications such as self-driving cars and personalized medicine. You will gain hands-on experience of applying these principles using Apache Spark, a resilient cluster computing system well suited for large-scale machine learning tasks. This book begins with a quick overview of setting up the necessary IDEs to facilitate the execution of code examples that will be covered in various chapters. It also highlights some key issues developers face while working with machine learning algorithms on the Spark platform. We progress by uncovering the various Spark APIs and the implementation of ML algorithms with developing classification systems, recommendation engines, text analytics, clustering, and learning systems. Toward the final chapters, we’ll focus on building high-end applications and explain various unsupervised methodologies and challenges to tackle when implementing with big data ML systems.
Table of Contents (20 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Streaming KMeans for a real-time on-line classifier


In this recipe, we explore the version of KMeans in used in unsupervised learning schemes. The purpose of streaming KMeans algorithm is to classify or group a set of data points into a number of clusters based on their similarity factor.

There are two implementations of the KMeans classification method, one for static/offline data and another version for continuously arriving, real-time updating data.

We will be streaming iris dataset clustering as new data streams into our streaming context.

How to do it...

  1. Start a new project in IntelliJ or in an IDE of your choice. Make sure that the necessary JAR files are included.
  1. Set up the package location where the program will reside:
package spark.ml.cookbook.chapter13
  1. Import the necessary packages:
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.rdd.RDD
import org.apache.spark.SparkContext
import scala.collection.mutable.Queue...