A SISSA algorithm published in Science simplifies the categorization of data
Experts use the expression big data to indicate huge amounts of information, such as those (photos, videos, texts, but also other more technical types of data) shared at any time by billions of people on computers, smartphones and other electronic devices. The present-day scenario offers unprecedented perspectives: tracking flu epidemics, monitoring road traffic in real time, or handling the emergency of natural disasters, for example. For us to be able to use these huge amounts of data, we have to understand them and before that we need to categorize them in an effective, fast and automatic manner. One of the most commonly used systems is a series of statistical techniques called Cluster Analysis (CA), which is able to group data sets according to their “similarity”.
Two researchers from SISSA devised a type of CA based on simple and powerful principles, which proved to be very efficient and capable of solving some of the most typical problems encountered in this type of analysis.
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