oftenmarvelous blogspot.com

Often Marvelous

Sunday, 11 July 2010. Bringing Home the Bacon using K-means clustering to solve spatial resource allocation problems. Was posted on proggit. Yesterday. The problem is to distribute a fixed number of bacon dispensers across a plane such that the sum of the distances of each population centre to their nearest bacon dispenser is minimized! Combinations of locations. For a grid size of 10 with 3 dispensers thats 161700 combinations, which is feasible. With N20 and B8 however, thats 1.5x10. K-means is no.

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Sunday, 11 July 2010. Bringing Home the Bacon using K-means clustering to solve spatial resource allocation problems. Was posted on proggit. Yesterday. The problem is to distribute a fixed number of bacon dispensers across a plane such that the sum of the distances of each population centre to their nearest bacon dispenser is minimized! Combinations of locations. For a grid size of 10 with 3 dispensers thats 161700 combinations, which is feasible. With N20 and B8 however, thats 1.5x10. K-means is no.

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This web page oftenmarvelous.blogspot.com states the following, "Bringing Home the Bacon using K-means clustering to solve spatial resource allocation problems." We saw that the webpage said " The problem is to distribute a fixed number of bacon dispensers across a plane such that the sum of the distances of each population centre to their nearest bacon dispenser is minimized! Combinations of locations." It also said " For a grid size of 10 with 3 dispensers thats 161700 combinations, which is feasible. With N20 and B8 however, thats 1."

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