Say you have a large corpus of web documents and you want to group them together by some notion of "similarity". For instance, we may want to detect plagiarism or find content that appears on multiple places on a site. In this scenario it is impractical to do a pairwise comparison of all documents. Fortunately, we can use simhashing.
Broadly speaking, simhashing is a algorithm that calculates a "group id" (the minimum hash, or minhash) from the content. Because the minhash for an item is calculated independently of the other items in the set, minhashing is an ideal candidate for MapReduce.
- See: http://www.xcombinator.com/2011/05/09/cascading-simhash-a-library-to-cluster-by-minhashes-in-hadoop/ for an introduction to this library.
- See: http://knol.google.com/k/simple-simhashing for more on simhashing.
leiningen
[cascading-simhash "1.0.0-SNAPSHOT"]
maven
<dependency>
<groupId>cascading-simhash</groupId>
<artifactId>cascading-simhash</artifactId>
<version>1.0.0-SNAPSHOT</version>
</dependency>
Take a look at src/java/simhash/examples/SimpleSimhash.java for an
example of how to use this package from java.
lein uberjar
lein classpath > classpath
java -cp `cat classpath`:build/cascading-simhash-1.0.0-SNAPSHOT-standalone.jar simhash.examples.SimpleSimhash "test-resources/test-documents.txt"
See: src/clj/simhash/examples/bigrams.clj
lein compile lein run -m simhash.examples.bigrams test-resources/test-documents.txt
Nate Murray 2011
Copyright 2010 Nate Murray
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