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    <p>Good plagiarism detection will apply heuristics based on the type of document (e.g. an essay or program code in a specific language).</p> <p>However, you can also apply a general solution. Have a look at the <a href="http://www.complearn.org/ncd.html" rel="noreferrer">Normalized Compression Distance</a> (NCD). Obviously you cannot exactly calculate a text's <a href="http://en.wikipedia.org/wiki/Kolmogorov_complexity" rel="noreferrer">Kolmogorov complexity</a>, but you can approach it be simply compressing the text.</p> <p>A smaller NCD indicates that two texts are more similar. Some compression algorithms will give better results than others. Luckily PHP provides support for <a href="http://us3.php.net/manual/en/refs.compression.php" rel="noreferrer">several</a> compression algorithms, so you can have your NCD-driven plagiarism detection code running in no-time. Below I'll give example code which uses <a href="http://en.wikipedia.org/wiki/Zlib" rel="noreferrer">Zlib</a>:</p> <p>PHP:</p> <pre><code>function ncd($x, $y) { $cx = strlen(gzcompress($x)); $cy = strlen(gzcompress($y)); return (strlen(gzcompress($x . $y)) - min($cx, $cy)) / max($cx, $cy); } print(ncd('this is a test', 'this was a test')); print(ncd('this is a test', 'this text is completely different')); </code></pre> <p>Python:</p> <pre><code>&gt;&gt;&gt; from zlib import compress as c &gt;&gt;&gt; def ncd(x, y): ... cx, cy = len(c(x)), len(c(y)) ... return (len(c(x + y)) - min(cx, cy)) / max(cx, cy) ... &gt;&gt;&gt; ncd('this is a test', 'this was a test') 0.30434782608695654 &gt;&gt;&gt; ncd('this is a test', 'this text is completely different') 0.74358974358974361 </code></pre> <p>Note that for larger texts (read: actual files) the results will be much more pronounced. Give it a try and report your experiences!</p>
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    1. CO+1. I read about compression being used to measure information content a long time ago but your post brought back this interesting idea.
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      1. USNoufal Ibrahim
 

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