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Thesis On Data Mining In Higher Education
so, what are the odds that on any given day someone in a list of 200000 will do something that will trigger suspicion? whether or not more than one suspicious act triggers on a given day, the likelihood of such random triggers is proportional to the number of suspicious acts they are screened for, as well as the number of people on the list.
This course is a great review of some popular probability distributions and how to test their assumptions. Reviewing the sampling techniques and testing the assumed distributions will help me when I check data and perform regression models.
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However, if your oncologist has you sent to a diagnostic center, where the specificity and sensitivity for the diagnostic test were the same as for the survey (but the prevalence there is substantial), then if your result is positive you should worry.
For admission to Master’s study courses providing further education, also evidence of qualified employment is required for a period of not less than one year as a rule.
Data Mining Applications in Higher Education
I don't think even that is true. Data mining does look good. In fact the real problem is that it looks to be so effective, it is a frightening to some.
> 2) Bruce talks alot about better tradeoffs -- well and good. However, a comparison is only valid to compare two things. It makes no sense to evaluate only data-minings effectivness -- we have to have something to which we can compare it to. Either we need a baseline number we're willing to use (e.g. cost per death prevented = $1000) or we need an alternative proposal to which we can compare cost.
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Chapter 6 Data Mining Applications in Higher Education Cali M
to the extent to which those parameters are supposed to be typical of the kinds of data mining being done, i do not for a second believe them. first of all, it is extraordinarily rare for probability of detection and probability of false alarm not to be within the same order of magnitude. second, the precision is limited by the number of candidates. surely it is meaningless to have a probability of *anything* which is less than 10^-8. third, i'd like to see a detailed calculation of how these are arrived at, using fictitious criteria if necessary but relevant and plausible ones. i bet it's done wrong. calculations of statistical power in these cases are not that easy, especially since the distributions of the parameters are never going to be Gaussian. hopefully, too, they're doing Bayesian decision theory, not Neymann-Pearson. fourth, even if an algorithm had a theoretical precision of that degree, their incoming data is not that precise. the accuracy depends upon the accuracy of given data as well as the algorithm.
Educational Data Mining Research
hey, you can be certain you've got the terrorists on your list if you just put everyone in the world on it. this is akin to forecasting "rain" every day (or "sunny") no matter what the evidence says. you're bound to be right at least the percentage of time it rains (or doesn't). this could be a real win in Seattle (or Tuscon).
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This isn't to say that such programs are risk free and don't require any sort of oversight. The most obvious risk is that a data source may have been collected historically on the understanding that it wasn't practicable to abuse it, and now it can be abused. The most obvious example is credit card transactions, logs of which are now being blatantly abused by marketers. So some oversight is required. But more intelligent analysis, which is what data mining is fundamentally about, is inherently much, much less harmful than the alternatives.
Silver Essay: Thesis Topics On Data Mining 100% …
For the record I also set a high price on civil liberty, but given that we mostly seem to agree that the authorities need to have some sort of investigative power, I find it absolutely bizarre that use of data mining should be seen as *more* intrusive than the alternatives, rather than much, much less intrusive. We are not talking about new surveillance programs, we are talking about making better use of data they already have, and probably even cutting back on some surveillance programs which prove not to be a good trade off. And we are not talking about squandering government money on pie in the sky schemes, we are talking about methods which inherently assess their own value and discard the useless parts, or at least allow citizens or their representatives to make better informed choices. And we aren't talking about something radical or new, we are talking about refinements of techniques which have been used in government for decades now, and by now are used by hundreds of government departments and thousands of businesses -- mostly for non-law enforcement purposes, but including dozens of law enforcement programs.
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