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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
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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    [james28909]: yep, i am scraping a websites date/time stamp from an article. none of that is relevant though because the strings i get are as expected. the returned data from the website is the list of @weekdays
    [james28909]: i need to convert those @weekdays to an numerical date based off of scalar localtime
    [james28909]: i have thught about this and though about it lol
    [james28909]: also remember, that if 'Today' is may 1, then Yesterday is not may 31 lol
    [james28909]: theoretically speaking ofcourse
    [james28909]: so if you subtracting as you loop, be aware that you will eventually be subtracting into a past month, or year <.<
    [james28909]: dontcha love it!

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