University of Utah researchers have developed a new multidimensional scaling method that they say enables simpler, faster data mining.
"The challenge of data mining is dealing with the dimensionality of the data and the volume of it," says Utah professor Suresh Venkatasubramanian. "What our approach does is unify into one common framework a number of different methods for doing this dimensionality reduction," which simplifies high-dimensional data, he says.
Using multidimensional scaling to simplify multidimensional data is an attempt "to reduce the dimensionality of data by finding key attributes defining most of the behavior," Venkatasubramanian says. "Prior methods on modern computers struggle with data from more than 5,000 people. Our method smoothly handles well above 50,000 people."
The researchers' new approach uses one set of instructions to perform a wide variety of multidimensional scaling that previously required separate instructions. It can handle large amounts of data because "rather than trying to analyze the entire set of data as a whole, we analyze it incrementally, sort of person by person," Venkatasubramanian says.
From University of Utah
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