Fan Zhang, Junwei Cao, et al.
IEEE TETC
We consider the problem of finding a set of attribute values that give a high quality binary segmentation of a database. The quality of a segmentation is defined by an objective function suitable for the user's objective, such as "mean squared error," "mutual information," or "χ2," each of which is defined in terms of the distribution of a given target attribute. Our goal is to find value groups on a given conditional domain that split databases into two segments, optimizing the value of an objective function. Though the problem is intractable for general objective functions, there are feasible algorithms for finding high quality binary segmentations when the objective function is convex, and we prove that the typical criteria mentioned above are all convex. We propose two practical algorithms, based on computational geometry techniques, which find a much better value group than conventional heuristics.
Fan Zhang, Junwei Cao, et al.
IEEE TETC
Michael C. McCord, Violetta Cavalli-Sforza
ACL 2007
Kafai Lai, Alan E. Rosenbluth, et al.
SPIE Advanced Lithography 2007
Sai Zeng, Angran Xiao, et al.
CAD Computer Aided Design