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Java: Data Science Made Easy

You're reading from   Java: Data Science Made Easy Data collection, processing, analysis, and more

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Product type Course
Published in Jul 2017
Publisher Packt
ISBN-13 9781788475655
Length 734 pages
Edition 1st Edition
Languages
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Authors (3):
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Alexey Grigorev Alexey Grigorev
Author Profile Icon Alexey Grigorev
Alexey Grigorev
Richard M. Reese Richard M. Reese
Author Profile Icon Richard M. Reese
Richard M. Reese
Jennifer L. Reese Jennifer L. Reese
Author Profile Icon Jennifer L. Reese
Jennifer L. Reese
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Toc

Table of Contents (29) Chapters Close

Title Page
Credits
Preface
1. Module 1 FREE CHAPTER
2. Getting Started with Data Science 3. Data Acquisition 4. Data Cleaning 5. Data Visualization 6. Statistical Data Analysis Techniques 7. Machine Learning 8. Neural Networks 9. Deep Learning 10. Text Analysis 11. Visual and Audio Analysis 12. Visual and Audio Analysis 13. Mathematical and Parallel Techniques for Data Analysis 14. Bringing It All Together 15. Module 2
16. Data Science Using Java 17. Data Processing Toolbox 18. Exploratory Data Analysis 19. Supervised Learning - Classification and Regression 20. Unsupervised Learning - Clustering and Dimensionality Reduction 21. Working with Text - Natural Language Processing and Information Retrieval 22. Extreme Gradient Boosting 23. Deep Learning with DeepLearning4J 24. Scaling Data Science 25. Deploying Data Science Models 26. Bibliography

Chapter 20. Unsupervised Learning - Clustering and Dimensionality Reduction

In the previous chapter, covered with with machine learning in Java and discussed how to approach the supervised learning problem when the label information is provided.

Often, however, there is no label information, and all we have is just some data. In this case, it is still possible to use machine learning, and this class of problems is called unsupervised learning; there are no labels, hence no supervision. Cluster analysis belongs to one of these algorithms. Given some dataset, the goal is to group the items from there such that similar items are put into the same group.

Additionally, some unsupervised learning techniques can be useful when there is label information.

For example, the dimensionality reduction algorithm tries to compress the dataset such that most of the information is preserved and the dataset can be represented with fewer features. What is more, dimensionality reduction is also useful for performing...

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