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You're reading from  scikit-learn Cookbook - Second Edition

Product typeBook
Published inNov 2017
Reading LevelIntermediate
PublisherPackt
ISBN-139781787286382
Edition2nd Edition
Languages
Python
Tools
Scikit-learn
Concepts
Machine Learning
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Author (1)
Trent Hauck
Trent Hauck
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Trent Hauck

Trent Hauck is a data scientist living and working in the Seattle area. He grew up in Wichita, Kansas and received his undergraduate and graduate degrees from the University of Kansas. He is the author of the book Instant Data Intensive Apps with pandas How-to, Packt Publishing—a book that can get you up to speed quickly with pandas and other associated technologies.
Read more about Trent Hauck

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Using MiniBatch k-means to handle more data

K-means is a nice method to use; however, it is not ideal for a lot of data. This is due to the complexity of k-means. This said, we can get approximate solutions with much better algorithmic complexity using MiniBatch k-means.

Getting ready

MiniBatch k-means is a faster implementation of k-means. K-means is computationally very expensive; the problem is NP-hard.

However, using MiniBatch k-means, we can speed up k-means by orders of magnitude. This is achieved by taking many subsamples that are called MiniBatches. Given the convergence properties of subsampling, a close approximation to regular k-means is achieved provided there are good initial conditions.

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scikit-learn Cookbook - Second Edition
Published in: Nov 2017Publisher: PacktISBN-13: 9781787286382
© 2017 Packt Publishing Limited All Rights Reserved

Author (1)

author image
Trent Hauck

Trent Hauck is a data scientist living and working in the Seattle area. He grew up in Wichita, Kansas and received his undergraduate and graduate degrees from the University of Kansas. He is the author of the book Instant Data Intensive Apps with pandas How-to, Packt Publishing—a book that can get you up to speed quickly with pandas and other associated technologies.
Read more about Trent Hauck

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