We consider the problem of finding all maximal empty rectangles in large, two-dimensional data sets. We introduce a novel, scalable algorithm for finding all.

Mar 14, 2003 · We consider the problem of finding all maximal empty rectangles in large, two-dimensional data sets. We introduce a novel, scalable algorithm ...

We consider the problem of finding all maximal empty rectangles in large, two-dimensional data sets. We introduce a novel, scalable algorithm for finding all ...

We consider the problem of finding all maximal empty rectangles in large, two-dimensional data sets. We introduce a novel, scalable algorithm for finding all ...

Our algorithm requires a single scan of a sorted data set and uses a small, bounded amount of memory to compute the set of all maximal empty rectangles. In ...

We consider the problem of finding all maximal empty rectangles in large, two-dimensional data sets. We introduce a novel, scalable algorithm for finding all ...

To maximize the use of empty space knowledge, our goal in this work is to not only find empty regions in the data, but to fully characterize that empty space.

Mining for Empty Spaces in Large Data Sets ; Publication Type, Journal Article ; Year of Publication, 2003 ; Authors, Edmonds, J., J. Gryz, D. Liang, and R. Miller.

We consider the problem of finding all maximal empty rectangles in large, two-dimensional data sets. We introduce a novel, scalable algorithm for finding all ...

Mining for empty spaces in large data sets. Edmonds, J., Gryz, J., Liang, D., & Miller, R. J. Theor. Comput. Sci., 296(3):435–452, 2003.