Overview of Data Mining

What was formerly confined to the lab and the socially awkward in white coats has found its way in to a range of areas. These areas ranging from the very small (genomics) to the infinitely large (astrophysics) and from the mundane (customer relationship management) to the very specialized (aviation assistance for pilots). Data mining has found application in industries from the most open, like e-commerce and census analysis, to the most secret, such as anti-terrorism and fraud detection.

Okay, that’s the where, but why? Well, mainly to aid with decision-making by making the most of complex information. It limits human subjectivity in the decision-making process and can handle large amounts of data with increased speed, thanks to the power of computers. Even the one you are reading this blog on is capable enough to do many data mining and analysis tasks. You could say simply that data mining is the art of extracting information, or knowledge, from data.

Have you have ever been scrolling through Facebook and found ads for products that you were searching for in the last few days; yeah, data mining was responsible. Or maybe while reviewing your Amazon shopping cart (with a new pair of shoes) and you see a suggestion for socks. Yep, data mining, again.

You can find it in some of the most unexpected places. One that even surprised me is the food industry. They use sensory analysis, as perceived by consumers and as measured by physical and chemical instruments to find consumer preferences for various products. Discriminant analysis and logistic regression predictive models (I told you to tie your laces extra tight) are also used in the drinks industry to distinguish the authentic from the counterfeit products, based on the analysis of about ten molecules present in the beverage.

The list of applications is nearly endless, so I wont try to fit it into one post, or even this series. But it should give you enough information to make you dangerous.

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