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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

Clustering text

Clustering is the process of finding groups of objects that are similar to each other. The goal is that objects within a cluster should be more similar to each other than to objects in other clusters. Like classification, it is not a specific algorithm so much as a general class of algorithms that solve a general problem.

Although there are a variety of clustering algorithms, all rely to some extent on a distance measure. For an algorithm to determine whether two objects belong in the same or different clusters it must be able to determine a quantitative measure of the distance (or, if you prefer, the similarity) between them. This calls for a numeric measure of distance: the smaller the distance, the greater the similarity between two objects.

Since clustering is a general technique that can be applied to diverse data types, there are a large number of possible distance measures. Nonetheless, most data can be represented by one of a handful of common abstractions: a set,...

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