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Published inSep 2015
Reading LevelIntermediate
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ISBN-139781784397180
Edition1st Edition
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Henry Garner
Henry Garner
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Henry Garner

Henry Garner is a graduate from the University of Oxford and an experienced developer, CTO, and coach. He started his technical career at Britain's largest telecoms provider, BT, working with a traditional data warehouse infrastructure. As a part of a small team for 3 years, he built sophisticated data models to derive insight from raw data and use web applications to present the results. These applications were used internally by senior executives and operatives to track both business and systems performance. He then went on to co-found Likely, a social media analytics start-up. As the CTO, he set the technical direction, leading to the introduction of an event-based append-only data pipeline modeled after the Lambda architecture. He adopted Clojure in 2011 and led a hybrid team of programmers and data scientists, building content recommendation engines based on collaborative filtering and clustering techniques. He developed a syllabus and copresented a series of evening classes from Likely's offices for professional developers who wanted to learn Clojure. Henry now works with growing businesses, consulting in both a development and technical leadership capacity. He presents regularly at seminars and Clojure meetups in and around London.
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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.

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

Intra-cluster density

By contrast...

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Clojure for Data Science
Published in: Sep 2015Publisher: ISBN-13: 9781784397180

Author (1)

author image
Henry Garner

Henry Garner is a graduate from the University of Oxford and an experienced developer, CTO, and coach. He started his technical career at Britain's largest telecoms provider, BT, working with a traditional data warehouse infrastructure. As a part of a small team for 3 years, he built sophisticated data models to derive insight from raw data and use web applications to present the results. These applications were used internally by senior executives and operatives to track both business and systems performance. He then went on to co-found Likely, a social media analytics start-up. As the CTO, he set the technical direction, leading to the introduction of an event-based append-only data pipeline modeled after the Lambda architecture. He adopted Clojure in 2011 and led a hybrid team of programmers and data scientists, building content recommendation engines based on collaborative filtering and clustering techniques. He developed a syllabus and copresented a series of evening classes from Likely's offices for professional developers who wanted to learn Clojure. Henry now works with growing businesses, consulting in both a development and technical leadership capacity. He presents regularly at seminars and Clojure meetups in and around London.
Read more about Henry Garner