@article{cgosorio05curves,
title = {Visualization of High-Dimensional Data via Orthogonal Curves},
author = {César García-Osorio and Colin Fyfe},
url = {http://www.jucs.org/jucs_11_11/visualization_of_high_dimensional},
issn = {0948-695x},
year = {2005},
date = {2005-01-01},
journal = {Journal of Universal Computer Science},
volume = {11},
number = {11},
pages = {1806--1819},
abstract = {Computers are still much less useful than the ability of the human eye for pattern matching. This ability can be used quite straightforwardly to identify structure in a data set when it is two or three dimensional. With data sets with more than 3 dimensions some kind of transformation is always necessary. In this paper we review in depth and present and extension of one of these mechanisms: Andrews' curves. With the Andrews' curves we use a curve to represent each data point. A human can run his eye along a set of curves (representing the members of the data set) and identify particular regions of the curves which are optimal for identifying clusters in the data set. Of interest in this context, is our extension in which a moving three-dimensional image is created in which we can see clouds of data points moving as we move along the curves; in a very real sense, the data which dance together are members of the same cluster.},
keywords = {Andrews curves, Data Mining, Exploratory data analysis, Grand tour methods, Visual clustering},
pubstate = {published},
tppubtype = {article}
}

Computers are still much less useful than the ability of the human eye for pattern matching. This ability can be used quite straightforwardly to identify structure in a data set when it is two or three dimensional. With data sets with more than 3 dimensions some kind of transformation is always necessary. In this paper we review in depth and present and extension of one of these mechanisms: Andrews' curves. With the Andrews' curves we use a curve to represent each data point. A human can run his eye along a set of curves (representing the members of the data set) and identify particular regions of the curves which are optimal for identifying clusters in the data set. Of interest in this context, is our extension in which a moving three-dimensional image is created in which we can see clouds of data points moving as we move along the curves; in a very real sense, the data which dance together are members of the same cluster.

We use cookies to ensure that we give you the best experience on our website. If you continue to use this site we will assume that you are happy with it.Ok