@inbook{DataMiningSucesses2007,
title = {Successes and New Directions in Data Mining},
author = {César García-Osorio and Colin Fyfe},
url = {http://books.google.es/books?id=VCZrtbjTRp8C&pg=PA147&ots=Ap4Si7ucUU&dq=Success+and+New+Directions+in+Data+Mining&sig=-v34qqba2rihMBHTnyxGGLX-GaI#PPP1,M1},
doi = {10.3217/jucs-011-11-1806},
isbn = {1599046458},
year = {2007},
date = {2007-01-01},
pages = {236--276},
publisher = {Idea Group Inc.},
chapter = {Visualizing Multi Dimensional Data},
keywords = {Andrews curves, Data Mining, Data visualization, Exploratory data analysis},
pubstate = {published},
tppubtype = {inbook}
}

@inproceedings{VIIP2007,
title = {Adding Interactivity to Andrews Curves and Extensions},
author = {César García-Osorio and J L Benito-Esteban and Jesús Maudes and Juan José Rodríguez},
editor = {J J Villanueva},
url = {http://www.actapress.com/Abstract.aspx?paperId=31544},
isbn = {978--0--88986--691--1},
year = {2007},
date = {2007-00-01},
booktitle = {Visualization, Imaging, and Image Processing (VIIP 2007)},
pages = {118--122},
publisher = {ACTA Press},
address = {Palma de Mallorca},
keywords = {Andrews curves, Data Mining, Data visualization, Exploratory data analysis},
pubstate = {published},
tppubtype = {inproceedings}
}

@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.

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