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The Unsupervised Learning Workshop

You're reading from   The Unsupervised Learning Workshop Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781800200708
Length 550 pages
Edition 1st Edition
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Authors (3):
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Benjamin Johnston Benjamin Johnston
Author Profile Icon Benjamin Johnston
Benjamin Johnston
Christopher Kruger Christopher Kruger
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Christopher Kruger
Aaron Jones Aaron Jones
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Aaron Jones
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Toc

Table of Contents (11) Chapters Close

Preface
1. Introduction to Clustering 2. Hierarchical Clustering FREE CHAPTER 3. Neighborhood Approaches and DBSCAN 4. Dimensionality Reduction Techniques and PCA 5. Autoencoders 6. t-Distributed Stochastic Neighbor Embedding 7. Topic Modeling 8. Market Basket Analysis 9. Hotspot Analysis Appendix

Spatial Statistics

Spatial statistics is the branch of statistics that focuses on the analysis of data that has spatial properties, including geographic or topological coordinates. It is similar to time series analysis in that the goal is to analyze data that changes across some dimension. In the case of time series analysis, the dimension across which the data changes is time, whereas in the spatial statistics case, the data changes across the spatial dimension. There are a number of techniques that are included under the spatial statistics umbrella, but the technique we are concerned with here is kernel density estimation. As is the goal of most statistical analyses, in spatial statistics, we are trying to take samples of geographic data and use them to generate insights and make predictions. The analysis of earthquakes is one arena in which spatial statistical analyses are commonly deployed. By collecting earthquake location data, maps that identify areas of high and low earthquake...

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