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

[download]
vs. kvale's 
[12     -24     ] 33
[42     -76     ] 27
[80     -128    ] 14
[150    -250    ] 9
[280    -460    ] 9
[550    -950    ] 7
[1226   -2000   ] 3

[download]
12, 14, 16, 18, 18, 20, 20, 20, 20, 20, 20, 20, 22, 24, 24, 24, 24, 30, 30, 30, 32, 35, 35, 35, 35, 35, 35, 35, 36, 40, 42, 46, 48, 48, 50, 50, 50, 50, 54, 54, 55, 56, 56, 58, 58, 60, 60, 60, 60, 63, 64, 67, 67, 68, 70, 70, 76, 80, 86, 86, 86, 90, 90, 99, 100, 100, 100, 100, 104, 105, 128, 150, 150, 154, 169, 190, 200, 200, 200, 250, 280, 291, 291, 300, 325, 325, 330, 450, 460, 550, 566, 600, 700, 750, 770, 950, 1226, 1250, 2000, 15, 22, 24

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

In reply to Re^2: Making sense of data: Clustering OR A coding challenge by belg4mit
in thread Making sense of data: Clustering OR A coding challenge by belg4mit

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