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Applied Unsupervised Learning with Python

You're reading from   Applied Unsupervised Learning with Python Discover hidden patterns and relationships in unstructured data with Python

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Product type Paperback
Published in May 2019
Publisher
ISBN-13 9781789952292
Length 482 pages
Edition 1st Edition
Languages
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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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Table of Contents (12) Chapters Close

Applied Unsupervised Learning with Python
Preface
1. Introduction to Clustering FREE CHAPTER 2. Hierarchical Clustering 3. Neighborhood Approaches and DBSCAN 4. Dimension Reduction and PCA 5. Autoencoders 6. t-Distributed Stochastic Neighbor Embedding (t-SNE) 7. Topic Modeling 8. Market Basket Analysis 9. Hotspot Analysis Appendix

Introduction to DBSCAN


As mentioned in the previous section, the strength of DBSCAN becomes apparent when we analyze the benefits of taking a density-based approach to clustering. DBSCAN evaluates density as a combination of neighborhood radius and minimum points found in a neighborhood deemed a cluster.

This concept can be driven home if we re-consider the scenario where you are tasked with organizing an unlabeled shipment of wine for your store. In the past example, it was made clear that we can find similar wines based off their features, such as scientific chemical traits. Knowing this information, we can more easily group together similar wines and efficiently have our products organized for sale in no time. Hopefully, that is clear by now – but what may not have been clear is the fact that products that you order to stock your store often reflect real-world purchase patterns. To promote variety in your inventory, but still have enough stock of the most popular wines, there is a highly...

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