An adaptive method for unknown distributions in distributive partitioned sorting
Document Type
Article
Date of Original Version
1-1-1985
Abstract
Distributive Partitioned Sort (DPS) is a fast internal sorting algorithm which rung in 0(n) expected time on uniformly distributed data. Unfortunately, the method is biased toward such inputs, and its performance worsens as the data become increasingly nonuniform, such as with highly skewed distributions. An adaptation of DPS, which estimates the cumulative distribution function of the input data from a randomly selected sample, was developed and tested. The method runs only Y–4 percent slower than DPS in the uniform case, but outperforms DPS by 12–13 percent on exponentially distributed data for sufficiently large files. © 1985 IEEE.
Publication Title, e.g., Journal
IEEE Transactions on Computers
Volume
C-34
Issue
4
Citation/Publisher Attribution
Janus, Philip J., and Edmund A. Lamagna. "An adaptive method for unknown distributions in distributive partitioned sorting." IEEE Transactions on Computers C-34, 4 (1985): 367-372. doi: 10.1109/TC.1985.5009388.