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Apache Spark 2: Data Processing and Real-Time Analytics

You're reading from   Apache Spark 2: Data Processing and Real-Time Analytics Master complex big data processing, stream analytics, and machine learning with Apache Spark

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Product type Course
Published in Dec 2018
Publisher Packt
ISBN-13 9781789959208
Length 616 pages
Edition 1st Edition
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Authors (7):
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Sridhar Alla Sridhar Alla
Author Profile Icon Sridhar Alla
Sridhar Alla
Romeo Kienzler Romeo Kienzler
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Romeo Kienzler
Siamak Amirghodsi Siamak Amirghodsi
Author Profile Icon Siamak Amirghodsi
Siamak Amirghodsi
Broderick Hall Broderick Hall
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Broderick Hall
Md. Rezaul Karim Md. Rezaul Karim
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Md. Rezaul Karim
Meenakshi Rajendran Meenakshi Rajendran
Author Profile Icon Meenakshi Rajendran
Meenakshi Rajendran
Shuen Mei Shuen Mei
Author Profile Icon Shuen Mei
Shuen Mei
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Table of Contents (23) Chapters Close

Title Page
Copyright
About Packt
Contributors
Preface
1. A First Taste and What's New in Apache Spark V2 FREE CHAPTER 2. Apache Spark Streaming 3. Structured Streaming 4. Apache Spark MLlib 5. Apache SparkML 6. Apache SystemML 7. Apache Spark GraphX 8. Spark Tuning 9. Testing and Debugging Spark 10. Practical Machine Learning with Spark Using Scala 11. Spark's Three Data Musketeers for Machine Learning - Perfect Together 12. Common Recipes for Implementing a Robust Machine Learning System 13. Recommendation Engine that Scales with Spark 14. Unsupervised Clustering with Apache Spark 2.0 15. Implementing Text Analytics with Spark 2.0 ML Library 16. Spark Streaming and Machine Learning Library 1. Other Books You May Enjoy Index

Bisecting KMeans, the new kid on the block in Spark 2.0


In this recipe, we will download the glass dataset and try to identify and label each glass using a bisecting KMeans algorithm. The Bisecting KMeans is a hierarchical version of the K-Mean algorithm implemented in Spark using the BisectingKMeans() API. While this algorithm is conceptually like KMeans, it can offer considerable speed for some use cases where the hierarchical path is present.

The dataset we used for this recipe is the Glass Identification Database. The study of the classification of types of glass was motivated by criminological research. Glass could be considered as evidence if it is correctly identified. The data can be found at NTU (Taiwan), already in LIBSVM format.

How to do it...

  1. We downloaded the prepared data file in LIBSVM from: https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/glass.scale

The dataset contains 11 features and 214 rows.

  1. The original dataset and data dictionary is also available at...
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