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Applied Unsupervised Learning with R

You're reading from   Applied Unsupervised Learning with R Uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA

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Product type Paperback
Published in Mar 2019
Publisher
ISBN-13 9781789956399
Length 320 pages
Edition 1st Edition
Languages
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Authors (2):
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Bradford Tuckfield Bradford Tuckfield
Author Profile Icon Bradford Tuckfield
Bradford Tuckfield
Alok Malik Alok Malik
Author Profile Icon Alok Malik
Alok Malik
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Toc

Analytic Signatures


In Chapter 4, Dimension Reduction, we discussed dimension reduction – methods that enable us to express data succinctly in ways that give us insights into the data. A hash function, discussed previously, is yet another way to accomplish dimension reduction. Hash functions are effective for many purposes, including the file verification use case we discussed. In that scenario, we were interested in determining whether two scripts were exactly the same or not. Even a slight difference in data, such as changing the word "take" to "make," had the potential to completely corrupt the intended message, so exactness was required.

In other cases, we may want to make meaningful comparisons between different datasets without requiring the exact identity of the two datasets being compared. Consider the case of detecting copyright violations. Suppose that a website hosts images from its users. It wants to ensure that users are not submitting images that are protected by copyright....

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