Already a member? Log in

Sign up with your...

or

Sign Up with your email address

Add Tags

Duplicate Tags

Rename Tags

Share This URL With Others!

Save Link

Sign in

Sign Up with your email address

Sign up

By clicking the button, you agree to the Terms & Conditions.

Forgot Password?

Please enter your username below and press the send button.
A password reset link will be sent to you.

If you are unable to access the email address originally associated with your Delicious account, we recommend creating a new account.

Links 1 through 10 of 11 by Nathan Gilliatt tagged datamining

Using data to identify problems, not necessarily to solve them. Seems like a healthy approach.

Share It With Others!

"Essentially, GDELT is a massive list of important political events that have happened -- more than 200 million and counting -- identified by who did what to whom, when and where, drawn from news accounts and assembled entirely by software. Everything from a riot over food prices in Khartoum, to a suicide bombing in Sri Lanka, to a speech by the president of Paraguay goes into the system."

Share It With Others!

Share It With Others!

Share It With Others!

A new category of sites provides a user interface to open data sources. Expect more.

Share It With Others!

Impure is a visual programming language aimed to gather, process and visualize information, from user-owned data to diverse feeds in internet, including social media data, real time or historical financial information, images, news, search queries and many more.

Share It With Others!

An online book with a concept map for navigation. Looks like a good resource.

Share It With Others!

Research project at the University of Arizona attempts to collect all web data generated by Jihadist terrorist groups, applying multiple analytical approaches to the data.

Share It With Others!

Repeat after me: monitoring and analyzing social media are *not* just for marketing and PR. Companies are analyzing the data at the individual consumer level to make business decisions, too.

Share It With Others!

Scientific American article on machine interpretation of data. Key takeaway: systems should build hypotheses and revise them as new data arrives. "The key to making all this work is programming the system so that it never confuses original data with a conclusion inferred from those data."

Share It With Others!