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From Andrew Butkus <and...@butkus.co.uk>
Subject K-Means clustering explanation
Date Fri, 06 Dec 2013 20:33:44 GMT
Hi,

I’ve essentially taken the dataset i’ve pre-classified with naive bayes, they are news
article titles.


I would like to cluster these titles into categories such as …

‘murder’
‘sex’
‘fraud’
‘terrorism'


but am a little confused about the results i have received from my cluster dump (results attached),


I’m seeing a Top word list (which i specified to output), the list is somewhat irrelevant,
but has some of the keywords i’m looking to categorise
and underneath i have a key/value list (which i think is listing vectors / word weights).


How do i turn what I have processed into something useful? for example, how would i input
a title and return a category?

I’ve started to read through mahout in action book, but the book release I have seems to
be out of date for mahout 0.7.

I would also welcome any resources which shows how to do what I ask programmatically through
java.


Thanks

Andy


Top Terms:
                man                                     =>0.008000337010687452
                cdata                                   =>0.007997985964165618
                comments                                =>0.007731191320108965
                after                                   =>0.0069037602489741235
                school                                  => 0.00563160856893979
                bournemouth                             =>0.005011825189472135
                police                                  =>0.004733383314492858
                2013                                    =>0.0041557648663178405
                arrested                                =>0.004111181114247428
                home                                    =>0.003938528377541288
                manager                                 =>0.0039023473450242835
                poole                                   =>0.003582306689329533
                day                                     =>0.0034606181558174236
                dorset                                  =>0.003447262937763061
                time                                    =>0.0034374047718378543
                nurse                                   =>0.0033384290600343243
                back                                    =>0.003329673234220446
                videos                                  =>0.003327555941083277
                pictures                                =>0.0032607585027343176
                2                                       =>0.0032555880589821605
                business                                =>0.003155572579020411
                two                                     =>0.003042114366434562
                woman                                   =>0.0029723510044456745
                stats                                   => 0.00292934992316234
                avon                                    =>0.0029157652514312043
                charged                                 =>0.002882685347115821
                guilty                                  => 0.00283827426433915
                car                                     =>0.0028376236536839807
                1                                       =>0.0028258930566665338
                top                                     =>0.002809853497752466
                life                                    =>0.0027992448323098233
                off                                     =>0.0027682009102213863
                jobs                                    =>0.002732444673670308
                court                                   =>0.0027162454947557654
                crash                                   =>0.002699148979402604
                about                                   =>0.0026593731688505325
                death                                   =>0.0025994461863060635
                care                                    =>0.0025684477030354608
                case                                    =>0.0025439029865843036
….

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joyce:0.312, penalty:0.2$
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benefits:0.400, fraud:0.353, women:0.365]
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hip:0.390, inmates:0.442, prison:0.317, shower:0.468, through:0.331]
        1.0: /negative_article/0051048557ca1d3ef622e46881e1311d = [after:0.303, charged:0.381,
crash:0.387, injured:0.460, man:0.303, officer:0.444, police:0.337]
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drugs:0.434, gets:0.357, guns:0.495]
        1.0: /negative_article/0056080c48797ee9dd6695a458067b70 = [being:0.241, dreadful:0.378,
evens:0.401, fulfilled:0.388, george:0.257, licensed:0.382, newspapers:0.347, prophecy:0.394]
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floods:0.532, hundreds:0.452]
        1.0: /negative_article/005916c054486fedf7727a265eb78f16 = [bananas:0.451, crate:0.478,
frog:0.467, jumps:0.376, supermarket:0.339, tree:0.305]
        1.0: /negative_article/005c485c71df1b163c430cfbbf85deb0 = [arrested:0.276, bust:0.419,
child:0.327, porn:0.386, ring:0.393, victorians:0.581]
        1.0: /negative_article/005ddcb0fb22f5dc21575bc3ade944b2 = [airport:0.455, axe:0.549,
bournemouth:0.316, face:0.404, seven:0.478]
        1.0: /negative_article/005f305f1619838261497f731f985187 = [eastern:0.482, gets:0.332,
kentucky:0.486, tsu:0.650]
        1.0: /negative_article/006197c31d29c4c5009a30f429f31ffd = [deported:0.418, mistakenly:0.486,
mom:0.349, reunites:0.523, teen:0.300, texas:0.321]
        1.0: /negative_article/00630196962c6d8f5a475a6dfd8f0fe8 = [beat:0.299, dallas:0.346,
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stable:0.512, victim:0.389]
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missoula:0.578, montana:0.553, sports:0.292, weather:0.308]
        1.0: /negative_article/006b81cdc2684fc6bea4f6241e021669 = [90:0.539, arrested:0.341,
montreal:0.613, protest:0.467]


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