An evolutionary cluster validation index
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Oh:2008:BICTA,
-
author = "Sanghoun Oh and Chang Wook Ahn and Moongu Jeon",
-
title = "An evolutionary cluster validation index",
-
booktitle = "3rd International Conference on Bio-Inspired
Computing: Theories and Applications, BICTA 2008",
-
year = "2008",
-
month = "28 " # sep # "-1 " # oct,
-
pages = "83--88",
-
keywords = "genetic algorithms, genetic programming, evolutionary
cluster validation index, fitness function, random
factors, training data set, pattern clustering",
-
DOI = "doi:10.1109/BICTA.2008.4656708",
-
abstract = "This paper presents a new evolutionary method for the
cluster validation index (CVI), namely eCVI. The
proposed method learns CVI from the generated training
data set using the genetic programming (GP), and then
outputs the optimal number of clusters after taking
parameters of a test data set into the learned CVI.
Each chromosome encodes a possible CVI as a function of
the number of clusters, density measure of clusters,
and some random factors. Fitness function evaluating
each candidate is defined by the difference between the
actual number of clusters from training data set and
the number of clusters computed by the current CVI.
Because of the adaptive nature of GP, the proposed eCVI
is reliable and robust in various types of data sets.
Experimental results provide grounds for the dominance
of eCVI over several widely-known CVIs.",
-
notes = "Also known as \cite{4656708}",
- }
Genetic Programming entries for
Sanghoun Oh
Chang Wook Ahn
Moongu Jeon
Citations