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posted by Fnord666 on Tuesday December 13 2016, @07:46AM   Printer-friendly
from the come-on-feel-the-noize dept.

This insight into the information which can be gleaned from data is cool and worrisome by equal measures.

Early in his talk, computer scientist John Hopcroft noted a funny fact about clustering algorithms: they work better on synthetic data than real data. But this is more than an odd tidbit about software.

[...] When we invent our own synthetic data, we try to mimic real data by mixing true information with random distraction–combining "signal" with "noise." But in real data, the divide isn't so clear. What often looks like noise turns out to be the deep structure we haven't grasped yet.

Hopcroft's insight: data doesn't just have one structure. It has many. If I scanned notebooks from a hundred people, and made a database of all the individual letters, I could sort them lots of ways. Alphabetically. Capital/lowercase. Size. Darkness. Handwriting. Each of these is a different layer of structure.

And to understand data–and the world–you've got to reckon with all those layers.

The part of the video which discusses the above starts around 5:45.

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  • (Score: 2) by VLM on Tuesday December 13 2016, @12:48PM

    by VLM (445) Subscriber Badge on Tuesday December 13 2016, @12:48PM (#440766)

    I'm not watching the video, too slow.

    John Hopcroft

    Ah ha that name sounded familiar and I took an automata theory class with Ullman, and Ullman and Hopcroft coauthored the (currently dominant?) textbook on automata theory. I always figured Hopcroft was an automata guy but most CS folks are pretty wide ranging so I guess its no great surprise he's talking randomness and stuff.

    At least from the summary, look on the bright side, you're a lot better off if your clustering algo proves your measured randomness isn't random than if you clustering algo proves your artificial randomness source isn't random.

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  • (Score: 1) by charon on Tuesday December 13 2016, @06:21PM

    by charon (5660) on Tuesday December 13 2016, @06:21PM (#440901) Journal
    I included the video link for completeness' sake, but the interesting portion of the talk is the part written about by the article author.
    • (Score: 2) by VLM on Tuesday December 13 2016, @06:44PM

      by VLM (445) Subscriber Badge on Tuesday December 13 2016, @06:44PM (#440920)

      I was thinking more about the topic and its interesting that he specifically mentioned clustering algos as being kinda unreliable whereas stuff like telecom/rf data transfer proves out deep into the decimal places.

      They should be very similar, after all here's an analog stream of QAM where you cluster the instantaneous phase and amplitude into certain bins where each bin passes multiple bits of data, and it works very well in practice. So you'd expect that a more generic clustering algo fed something other than a telecom RF data feed would work as well or better but he implies theres a lot of fuzzy going on such that it doesn't.

      Can't even use the excuse of low SNR, thanks to the physicists and EEs you can do crazy low SNR telecom stuff with space probes that works out to the theoretical limit pretty well indeed... unless you believe the whole space program / moon landing stuff is a hoax. So crazy low SNR census data correlations or whatever should cluster well, like, hey why not?