S. Malinchik, B. Orme, J. Rothermich, and E. Bonabeau. Proceedings of the 2004 IEEE Congress on Evolutionary
Computation, page 1098--1104. Portland, Oregon, IEEE Press, (20-23 June 2004)
Abstract
We illustrate with two simple examples how Interactive
Evolutionary Computation (IEC) can be applied to
Exploratory Data Analysis (EDA). IEC is valuable in an
EDA context because the objective function is by
definition either unknown a priori or difficult to
formalize. In the first example IEC is used to evolve
the "true" metric of attribute space. The goal here
is to evolve the attribute space distance function
until "interesting" features of the data are
revealed when a clustering algorithm is applied. In a
second example, we show how a user can interactively
evolve an auditory display of cluster data. In this
example, we use IEC with Genetic Programming to evolve
a mapping of data to sound for sonifying qualities of
data clusters.
%0 Conference Paper
%1 malinchik:2004:ieda
%A Malinchik, Sergey
%A Orme, Belinda
%A Rothermich, Joseph
%A Bonabeau, Eric
%B Proceedings of the 2004 IEEE Congress on Evolutionary
Computation
%C Portland, Oregon
%D 2004
%I IEEE Press
%K Real-world algorithms, applications genetic programming,
%P 1098--1104
%T Interactive Exploratory Data Analysis
%X We illustrate with two simple examples how Interactive
Evolutionary Computation (IEC) can be applied to
Exploratory Data Analysis (EDA). IEC is valuable in an
EDA context because the objective function is by
definition either unknown a priori or difficult to
formalize. In the first example IEC is used to evolve
the "true" metric of attribute space. The goal here
is to evolve the attribute space distance function
until "interesting" features of the data are
revealed when a clustering algorithm is applied. In a
second example, we show how a user can interactively
evolve an auditory display of cluster data. In this
example, we use IEC with Genetic Programming to evolve
a mapping of data to sound for sonifying qualities of
data clusters.
%@ 0-7803-8515-2
@inproceedings{malinchik:2004:ieda,
abstract = {We illustrate with two simple examples how Interactive
Evolutionary Computation (IEC) can be applied to
Exploratory Data Analysis (EDA). IEC is valuable in an
EDA context because the objective function is by
definition either unknown a priori or difficult to
formalize. In the first example IEC is used to evolve
the {"}true{"} metric of attribute space. The goal here
is to evolve the attribute space distance function
until {"}interesting{"} features of the data are
revealed when a clustering algorithm is applied. In a
second example, we show how a user can interactively
evolve an auditory display of cluster data. In this
example, we use IEC with Genetic Programming to evolve
a mapping of data to sound for sonifying qualities of
data clusters.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Portland, Oregon},
author = {Malinchik, Sergey and Orme, Belinda and Rothermich, Joseph and Bonabeau, Eric},
biburl = {https://www.bibsonomy.org/bibtex/2f47e2563ae2be0aa0853919a87c8f2f3/brazovayeye},
booktitle = {Proceedings of the 2004 IEEE Congress on Evolutionary
Computation},
interhash = {82f404143431848a21d51b01cd1d3b6b},
intrahash = {f47e2563ae2be0aa0853919a87c8f2f3},
isbn = {0-7803-8515-2},
keywords = {Real-world algorithms, applications genetic programming,},
month = {20-23 June},
notes = {CEC 2004 - A joint meeting of the IEEE, the EPS, and
the IEE.},
pages = {1098--1104},
publisher = {IEEE Press},
timestamp = {2008-06-19T17:46:13.000+0200},
title = {Interactive Exploratory Data Analysis},
year = 2004
}