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This story is from the category Pure Research
Date posted: 25/07/2010 To many big companies, you aren't just a customer, but are described by multiple "dimensions" of information within a computer database. Now, a University of Utah computer scientist has devised a new method for simpler, faster "data mining," or extracting and analyzing massive amounts of such data. "Whether you like it or not, Google, Facebook, Walmart and the government are building profiles of you, and these consist of hundreds of attributes describing you" - your online searches, purchases, shared videos and recommendations to your Facebook friends, says Suresh Venkatasubramanian, an assistant professor of computer science. "If you line them up for each person, you have a line of hundreds of numbers that paint a picture of a person: who they are, what their interests are, who their friends are and so forth," he says. "These strings of hundreds of attributes are called high-dimensional data because each attribute is called one dimension. Data mining is about digging up interesting information from this high-dimensional data." A group of data-mining methods named "multidimensional scaling" or MDS first was used in the 1930s by psychologists and has been used ever since to make data analysis simpler by reducing the "dimensionality" of the data. Venkatasubramanian says it is "probably one of the most important tools in data mining and is used by countless researchers everywhere." Now, Venkatasubramanian and colleagues have devised a new method of multidimensional scaling that is faster, simpler, can be used universally for numerous problems and can handle more data, basically by "squashing things [data] down to size." See the full Story via external site: www.physorg.com Most recent stories in this category (Pure Research): 08/02/2017: New algorithms by U of T researchers may revolutionize drug discoveries |
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