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Theses Related to Data Mining and Database Systems
The high rates of false-positives from this data mining will provide excellent cover for the selective pursuit of a manageable subset of the mountain of investigatory leads. This huge ocean of "leads" will bring new meaning to the phrase, "the usual suspects."
Absolutely, and to be proper we should be talking about data collection as the primary problem (rather, the issue to which I take exception), not the analyzation techniques. Of course, you have to have the data to mine and you have to get the data from some source. When I use the term "surveillance", I'm referring to the government going out and sucking up as much data as they can get their hands on, from Google/Yahoo's search requests, to Amazon's buy lists, etc. under the assumption that the more data they have, the better. As you yourself point out, a vast majority of this data is going to be unanalyzed for/by any offical purpose.
How to choose a good thesis topic in Data Mining? - …
Thanks Brian, and the answer is yes – since your algorithm is bound to your subset. And if you linearly change your subset before using it, you can change it back linearly afterwards, it is technically not interfering with your algorithm in any way. So it does not depend on any data mining technique. Though chances are high that your result will be different (and better, hopefully). If you are into this you might want to read by Gary King and Langche Zeng.
Guido, nice post.
It is a useful formula, but I quit using it some years ago, because the probabilities calculated by the model are only the probabilities of the training data. To know the real value of the model, I always check it against a second dataset containing new data that are more recent than the training data. For it is obvious: you want to use the model to predict the future, so you should check your model against data from the future. The real world validation is the only thing you can trust in data mining, otherwise you never know whether the model is time-robust, overfitted etc.
About simply duplicating records : plainly put : it ads no information whatsoever, it only consumes diskspace. If you want to duplicate records you should do it in an intelligent way. This is nicely described by Dorian Pyle in his book “”Data Preparation for Data Mining”, which I still find the best data mining book I ever laid eyes on.
Student Theses Related to Data Mining (Since 1996)
In the post 9/11 world, there's much focus on connecting the dots. Many believe that data mining is the crystal ball that will enable us to uncover future terrorist plots. But even in the most wildly optimistic projections, data mining isn't tenable for that purpose. We're not trading privacy for security; we're giving up privacy and getting no security in return.
This shouldn't be a surprise. In May 2004, the General Accounting Office that listed 122 different federal government data mining programs that used people's personal information. This list didn't include classified programs, like the NSA's eavesdropping effort, or state-run programs like .
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Theses Related to Data Mining (Since 1996) ..
Out of pure curiosity: what is the difference between oversampling and undersampling? I am asking because I found some others articles about data mining and it seems that some of them would rather call what you described here as undersampling . Basically dropping non-target variables would make a whole data set smaller thus undersampling. On the other hand, after oversampling (in example that you provided) would look more like this:
Thesis | Data Mining | Parallel Computing
The promise of data mining is compelling, and convinces many. But it's wrong. We're not going to find terrorist plots through systems like this, and we're going to waste valuable resources chasing down false alarms. To understand why, we have to look at the economics of the system.
Master Thesis In Data Mining - …
Security is always a trade-off, and for a system to be worthwhile, the advantages have to be greater than the disadvantages. A national security data mining program is going to find some percentage of real attacks, and some percentage of false alarms. If the benefits of finding and stopping those attacks outweigh the cost -- in money, liberties, etc. -- then the system is a good one. If not, then you'd be better off spending that cost elsewhere.
Text & Data Mining of PhD theses - has anyone tried it?
Data mining works best when there's a well-defined profile you're searching for, a reasonable number of attacks per year, and a low cost of false alarms. Credit card fraud is one of data mining's success stories: all credit card companies data mine their transaction databases, looking for spending patterns that indicate a stolen card. Many credit card thieves share a pattern -- purchase expensive luxury goods, purchase things that can be easily fenced, etc. -- and data mining systems can minimize the losses in many cases by shutting down the card. In addition, the cost of false alarms is only a phone call to the cardholder asking him to verify a couple of purchases. The cardholders don't even resent these phone calls -- as long as they're infrequent -- so the cost is just a few minutes of operator time.
A data model explicitly determines the structure of data
Terrorist plots are different. There is no well-defined profile, and attacks are very rare. Taken together, these facts mean that data mining systems won't uncover any terrorist plots until they are very accurate, and that even very accurate systems will be so flooded with false alarms that they will be useless.
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