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Mastering Apache Spark 2.x - Second Edition

You're reading from  Mastering Apache Spark 2.x - Second Edition

Product type Book
Published in Jul 2017
Publisher Packt
ISBN-13 9781786462749
Pages 354 pages
Edition 2nd Edition
Languages

Table of Contents (21) Chapters

Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. A First Taste and What’s New in Apache Spark V2 2. Apache Spark SQL 3. The Catalyst Optimizer 4. Project Tungsten 5. Apache Spark Streaming 6. Structured Streaming 7. Apache Spark MLlib 8. Apache SparkML 9. Apache SystemML 10. Deep Learning on Apache Spark with DeepLearning4j and H2O 11. Apache Spark GraphX 12. Apache Spark GraphFrames 13. Apache Spark with Jupyter Notebooks on IBM DataScience Experience 14. Apache Spark on Kubernetes

Clustering with K-Means


This example will use the same test data from the previous example, but we will attempt to find clusters in the data using the MLlib K-Means algorithm.

Theory on Clustering

The K-Means algorithm iteratively attempts to determine clusters within the test data by minimizing the distance between the mean value of cluster center vectors, and the new candidate cluster member vectors. The following equation assumes dataset members that range from X1 to Xn; it also assumes K cluster sets that range from S1 to Sk, where K <= n.

K-Means in practice

The K-Means MLlib functionality uses the LabeledPoint structure to process its data and so it needs numeric input data. As the same data from the last section is being reused, we will not explain the data conversion again. The only change that has been made in data terms in this section, is that processing in HDFS will now take place under the /data/spark/kmeans/ directory. Additionally, the conversion Scala script for the K-Means...

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