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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

Jaccard similarity for large sets with MinHash

The Bloom filter is a probabilistic data structure to determine whether an item is a member of a set. While comparing user or item similarities, what we are usually interested in is the intersection between sets, as opposed to their precise contents. MinHash is a technique that enables a large set to be compressed in such a way that we can still perform the Jaccard similarity on the compressed representations.

Let's see how it works with a reference to two of the most prolific raters in the MovieLens dataset. Users 405 and 655 have rated 727 and 675 movies respectively. In the following code, we extract their ratings and convert them into sets before passing to Incanter's jaccard-index function. Recall that this returns the ratio of movies they've both rated out of all the movies they've rated:

(defn rated-items [user-ratings id]
  (->> (get user-ratings id)
       (map :item)))

(defn ex-7-22 []
  (let [ratings    ...
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