How does our brain cope with the enormous flux of information that bombards our senses? One important neural strategy is the ability to "cluster," or categorize, data and thus make sense of the world around us.
Prof. Eytan Domany, head of the Weizmann Institute's Physics of Complex Systems Department, has developed a new method, or algorithm, for performing "clustering" on computer. A patent application for the algorithm, whose physical aspects are described in the April issue of Physical Review E, has been filed through Yeda Research and Development Co., the Institute's technology transfer arm. The approach has great potential for use in data-heavy scientific and industrial applications.
For example, the algorithm may be used to analyze the vast stream of information collected by satellites orbiting the earth. It may also be of great help in "data mining," the process by which specific information, such as details on a particular product, are culled from the world's huge and constantly growing commercial data banks.
One of the most interesting aspects of the new algorithm is the fact that it mimics unassisted learning. Unlike most automated "sorting" processes, in which a computer must be informed of the relevant categories in advance, Domany's algorithm is analogous to human intuition: it doesn't need to be told how the data is structured or how it should be broken down into groups. When confronted with each new clustering task, the algorithm analyzes the data, computes the degree of similarity between its components and picks its own criteria for breaking the data into clusters.
This is similar to the way in which a young child categorizes
unfamiliar objects. For example, a child who has never seen a
kangaroo or a bicycle, and is exposed to hundreds of different
pictures of each, will eventually figure out that the pictures
represent two types of objects -- in other words, that the pictures
form two "clusters," one
Contact: Julie Osler
American Committee for the Weizmann Institute of Science