Thinning out redundant empirical data

Details Thinning out redundant empirical data Thinning out redundant empirical data

2007-02-12

arxiv.org/abs/math/0702327v1

Mathematics in Computer Science, vol. 1 no. 2, pp. 375--392 (2007)

Mathematics

Algebraic Geometry

14 pages; 3 figures

Scientific paper

Given a set $X$ of "empirical" points, whose coordinates are perturbed by errors, we analyze whether it contains redundant information, that is whether some of its elements could be represented by a single equivalent point. If this is the case, the empirical information associated to $X$ could be described by fewer points, chosen in a suitable way. We present two different methods to reduce the cardinality of $X$ which compute a new set of points equivalent to the original one, that is representing the same empirical information. Though our algorithms use some basic notions of Cluster Analysis they are specifically designed for "thinning out" redundant data. We include some experimental results which illustrate the practical effectiveness of our methods.

Affiliated with

Also associated with

No associations

Rating

Thinning out redundant empirical data does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Thinning out redundant empirical data, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Thinning out redundant empirical data will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-633657