It's definitely a very valuable pass to make. It cuts down the combinatorics immensely, especially (as you said) if you can remove the most common words from the picture.

I implemented it myself, because I couldn't work out how to accumulate the partiton items on the fly using Graph::UnionFind--for good reason it seems:).

My homebrew implementation finds

  • 141 partitons in the 17576 keywords / 676 items set in .5 seconds.
  • 292 partitons in the 436697 keywords / 676 items set in 23 seconds.
  • 745 partitions in the 17676 keywords / 17676 items set in 18 seconds.
    [22:56:47.23] P:\test>436050-2 -W=3 -I=2 -NODETAILS Keywords: 16873 Items: 676 141 partitons found. [22:56:47.81] P:\test>436050-2 -W=4 -I=2 -NODETAILS Keywords: 438697 Items: 676 292 partitons found. [22:57:10.23] P:\test>436050-2 -W=3 -I=3 -NODETAILS Keywords: 16873 Items: 17576 745 partitons found. [22:57:28.48] P:\test>

    Which is good news because it means that it scales well in both directions.

    I tried the 438897K / 438697I combination, but even with avoiding the memory requirement of Gr::UnF's hashes, it still requires more memory than I have, and my disk is badly fragged so I am defragging it before trying again.

    Once you have partitioned, would you agree that it makes sense to do a pairwise combinations of the partitions and isolating those pairs within the partition that have nothing in common?


    Examine what is said, not who speaks.
    Silence betokens consent.
    Love the truth but pardon error.

    In reply to Re^7: algorithm for 'best subsets' by BrowserUk
    in thread algorithm for 'best subsets' by halley

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