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.
<dependency> <groupId>cascading-simhash</groupId> <artifactId>cascading-simhash</artifactId> <version>1.0.0-SNAPSHOT</version> </dependency>
Running the Java Example
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"
Running the Clojure Example
lein compile lein run -m simhash.examples.bigrams test-resources/test-documents.txt
Nate Murray 2011
Copyright 2010 Nate Murray This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>.