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Re: Making sense of data: Clustering OR A coding challenge

by codeacrobat (Chaplain)
on Apr 04, 2006 at 06:04 UTC ( #541084=note: print w/ replies, xml ) Need Help??


in reply to Making sense of data: Clustering OR A coding challenge

Something similar to k-means can be archieved with Statistics::Descriptive and its frequency_distribution.

use Statistics::Descriptive; $stat = Statistics::Descriptive::Full->new(); $stat->add_data( split /,/, <DATA> ); %f = $stat->frequency_distribution(4); $min = 0; for ( sort { $a <=> $b } keys %f ) { printf "[%d\t-%d\t] %d\n", $min, $_, $f{$_}; $min = $_ + 1; } __DATA__ 0, 12, 25, 38, 50, 62, 75, 88, 100 __END__ # prints [0 -25 ] 3 [26 -50 ] 2 [51 -75 ] 2 [76 -100 ] 2


Comment on Re: Making sense of data: Clustering OR A coding challenge
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Re^2: Making sense of data: Clustering OR A coding challenge
by belg4mit (Prior) on Apr 04, 2006 at 15:24 UTC
    Interesting, although it seems this method seems to favor outliers. With my sample dataset below it creates more bins with few or no items on the high end of the spectrum whereas nearly everything else continues to be lumped into the first category.
    [0 -296 ] 86 [297 -580 ] 8 [581 -864 ] 4 [865 -1148 ] 1 [1149 -1432 ] 2 [1433 -1716 ] 0 [1717 -2000 ] 1
    vs. kvale's
    [12 -24 ] 33 [42 -76 ] 27 [80 -128 ] 14 [150 -250 ] 9 [280 -460 ] 9 [550 -950 ] 7 [1226 -2000 ] 3

    --
    In Bob We Trust, All Others Bring Data.

      Then you might be interested in the
      $stat->frequency_distribution(\@bins);
      notation and add more bins at lower values.
        Except that the point was to find the natural bins inherent in the data :-P

        --
        In Bob We Trust, All Others Bring Data.

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