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Clojure for Data Science

You're reading from   Clojure for Data Science Statistics, big data, and machine learning for Clojure programmers

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Product type Paperback
Published in Sep 2015
Publisher
ISBN-13 9781784397180
Length 608 pages
Edition 1st Edition
Languages
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Author (1):
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Henry Garner Henry Garner
Author Profile Icon Henry Garner
Henry Garner
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Table of Contents (12) Chapters Close

Preface 1. Statistics FREE CHAPTER 2. Inference 3. Correlation 4. Classification 5. Big Data 6. Clustering 7. Recommender Systems 8. Network Analysis 9. Time Series 10. Visualization Index

Cluster evaluation measures

At the bottom of the file we looked at in the previous section, you'll see some statistics that suggest how well the data has been clustered:

Inter-Cluster Density: 0.6135607681542804
Intra-Cluster Density: 0.6957348405534836

These two numbers can be considered as the equivalent to the variance within and the variance between measures we have seen in Chapter 2, Inference and Chapter 3, Correlation. Ideally, we are seeking a lower variance (or a higher density) within clusters compared to the density between clusters.

Inter-cluster density

Inter-cluster density is the average distance between cluster centroids. Good clusters probably don't have centers that are too close to each other. If they did, it would indicate the clustering is creating groups with similar features, and perhaps drawing distinctions between cluster members that are hard to support.

Inter-cluster density

Thus, ideally our clustering will produce clusters with a large inter-cluster distance.

Intra-cluster density...

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