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Machine Learning With Go

You're reading from   Machine Learning With Go Implement Regression, Classification, Clustering, Time-series Models, Neural Networks, and More using the Go Programming Language

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781785882104
Length 304 pages
Edition 1st Edition
Languages
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Author (1):
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Joseph Langstaff Whitenack Joseph Langstaff Whitenack
Author Profile Icon Joseph Langstaff Whitenack
Joseph Langstaff Whitenack
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Table of Contents (11) Chapters Close

Preface 1. Gathering and Organizing Data 2. Matrices, Probability, and Statistics FREE CHAPTER 3. Evaluation and Validation 4. Regression 5. Classification 6. Clustering 7. Time Series and Anomaly Detection 8. Neural Networks and Deep Learning 9. Deploying and Distributing Analyses and Models 10. Algorithms/Techniques Related to Machine Learning

Understanding clustering model jargon

Clustering is quite unique and comes with it's own set of terms, which are shown below. Keep in mind that the following list is only a partial list as there are many different types of clustering with corresponding jargon:

  • Clusters or groups: Each of these clusters or groups is a collection of data points into which our clustering technique organizes our data points.
  • Intra-group or intra-cluster: Clusters resulting from clustering can be evaluated using a measure of similarity between data points and other data points in the same resulting cluster. This is called intra-group or intra-cluster evaluation and similarity.
  • Inter-groupor inter-cluster: Clusters resulting from clustering can be evaluated using a measure of dissimilarity between data points and other data points in other resulting clusters. This is called inter-group or inter...
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