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    <p>Approx 15 years ago I wrote fuzzy search, which can found N closes neighbors. This is my modification of Wilbur's trigram algorithm, and this modification named "Wilbur-Khovayko algorithm".</p> <p>Basic idea: To split strings by trigrams, and search maximal intersection scores.</p> <p>For example, lets we have string "hello world". This string is generates trigrams: hel ell llo "lo ", "o_w", eand so on; Also, produces special prefix/suffix trigrams for each word, like $he $wo lo$ ld$.</p> <p>Thereafter, for each trigram built index, in which term it is present.</p> <p>So, this is list of term_ID for each trigram.</p> <p>When user invoke some string - it also splits to trigrams, and program search maximal intersection score, and generates N-size list.</p> <p>It works quick: I remember, on old Sun/solaris, 256MB ram, 200MHZ CPU, it search 100 closest term in dictionary 5,000,000 terms, in 0.25s</p> <p>You can get my old source from: <a href="http://olegh.ftp.sh/wilbur-khovayko.tar.gz" rel="nofollow">http://olegh.ftp.sh/wilbur-khovayko.tar.gz</a></p> <p>UPDATE:</p> <p>I created new archive, where is Makefile adjusted for modern Linux/BSD make. You can download new version here: <a href="http://olegh.ftp.sh/wilbur-khovayko.tgz" rel="nofollow">http://olegh.ftp.sh/wilbur-khovayko.tgz</a></p> <p>Make some directory, and extract archive here:</p> <pre><code>mkdir F2 cd F2 tar xvfz wilbur-khovayko.tgz make </code></pre> <p>Go to test directory, copy term list file (this is fixed name, termlist.txt), and make index:</p> <pre><code> cd test/ cp /tmp/test/termlist.txt ./termlist.txt ./crefdb.exe &lt;termlist.txt </code></pre> <p>In this test, I used ~380,000 expired domain names:</p> <pre><code>wc -l termlist.txt 379430 termlist.txt </code></pre> <p>Run findtest application:</p> <pre><code>./findtest.exe boking &lt;-- this is query -- word "booking" with misspeling 0001:Query: [boking] 1: 287890 ( 3.863739) [bokintheusa.com,2009-11-20,$69] 2: 287906 ( 3.569148) [bookingseu.com,2009-11-20,$69] 3: 257170 ( 3.565942) [bokitko.com,2009-11-18,$69] 4: 302830 ( 3.413791) [bookingcenters.com,2009-11-21,$69] 5: 274658 ( 3.408325) [bookingsadept.com,2009-11-19,$69] 6: 100438 ( 3.379371) [bookingresorts.com,2009-11-09,$69] 7: 203401 ( 3.363858) [bookinginternet.com,2009-11-15,$69] 8: 221222 ( 3.361689) [bobokiosk.com,2009-11-16,$69] . . . . 97: 29035 ( 2.169753) [buccupbooking.com,2009-11-05,$69] 98: 185692 ( 2.169047) [box-hosting.net,2009-11-14,$69] 99: 345394 ( 2.168371) [birminghamcookinglessons.com,2009-11-25,$69] 100: 150134 ( 2.167372) [bowlingbrain.com,2009-11-12,$69] </code></pre>
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    1. POFast fuzzy/approximate search in dictionary of strings in Ruby
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    1. COVery interesting! Why don't you put it on public source code platform like f.e. GitHub, Gitorious, or Bitbucket?
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      1. USmdesantis
    2. COIt had been written ~15 years ago, when github, etc was not exist. I deposited it on my own site. If you wish, you can get the source, and turn it into public open source project.
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    3. COInteresting. Could you explain how I could run your code on some examples (for someone who doesn't remember much about C... :)) Thanks!
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      1. USNicolas M.
 

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