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Mastering Python Data Visualization

You're reading from   Mastering Python Data Visualization Generate effective results in a variety of visually appealing charts using the plotting packages in Python

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Product type Paperback
Published in Oct 2015
Publisher
ISBN-13 9781783988327
Length 372 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (11) Chapters Close

Preface 1. A Conceptual Framework for Data Visualization 2. Data Analysis and Visualization FREE CHAPTER 3. Getting Started with the Python IDE 4. Numerical Computing and Interactive Plotting 5. Financial and Statistical Models 6. Statistical and Machine Learning 7. Bioinformatics, Genetics, and Network Models 8. Advanced Visualization A. Go Forth and Explore Visualization Index

Principal component analysis


Principal component analysis (PCA) transforms the attributes of unlabeled data using a simple rearrangement and transformation with rotation. Looking at the data that does not have any significance, you can find ways to reduce dimensions this way. For instance, when a particular dataset looks similar to an ellipse when run at a particular angle to the axes, while in another transformed representation moves along the x axis and clearly has signs of no variation along the y axis, then it may be possible to ignore that.

k-means clustering is appropriate to cluster unlabeled data. Sometimes, one can use PCA to project data to a much lower dimension and then apply other methods, such as k-means, to a smaller and reduced data space.

However, it is very important to perform dimension reduction carefully because any dimension reduction may lead to the loss of information, and it is crucial that the algorithm preserves the useful part of the data while discarding the noise...

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