In previous work, the authors have introduced a novel and highly effective approach to data clustering, based on the explicit optimization of a partitioning with respect to two complementary clustering objectives (Handl, et. al., 2004, 2005). In this paper, three modifications were made to the algorithm that improved its scalability to large data sets with high dimensionality and large numbers of clusters. Specifically, new initialization and mutation schemes that enable a more efficient exploration of the search space were introduced, and the data model that is used as a basis for selecting the most significant solution from the Pareto front was modified. The high performance of the resulting algorithm is demonstrated on a newly developed clustering test suite.
Описание
Welcome to IEEE Xplore 2.0: Improvements to the scalability of multiobjective clustering
%0 Journal Article
%1 Handl05multiobjectiveClustering
%A Handl, J.
%A Knowles, J.
%D 2005
%J Evolutionary Computation, 2005. The 2005 IEEE Congress on
%K 05 Handl clustering multiobjective
%P 2372-2379
%R 10.1109/CEC.2005.1554990
%T Improvements to the scalability of multiobjective clustering
%V 3
%X In previous work, the authors have introduced a novel and highly effective approach to data clustering, based on the explicit optimization of a partitioning with respect to two complementary clustering objectives (Handl, et. al., 2004, 2005). In this paper, three modifications were made to the algorithm that improved its scalability to large data sets with high dimensionality and large numbers of clusters. Specifically, new initialization and mutation schemes that enable a more efficient exploration of the search space were introduced, and the data model that is used as a basis for selecting the most significant solution from the Pareto front was modified. The high performance of the resulting algorithm is demonstrated on a newly developed clustering test suite.
@article{Handl05multiobjectiveClustering,
abstract = { In previous work, the authors have introduced a novel and highly effective approach to data clustering, based on the explicit optimization of a partitioning with respect to two complementary clustering objectives (Handl, et. al., 2004, 2005). In this paper, three modifications were made to the algorithm that improved its scalability to large data sets with high dimensionality and large numbers of clusters. Specifically, new initialization and mutation schemes that enable a more efficient exploration of the search space were introduced, and the data model that is used as a basis for selecting the most significant solution from the Pareto front was modified. The high performance of the resulting algorithm is demonstrated on a newly developed clustering test suite.},
added-at = {2008-12-03T22:16:47.000+0100},
author = {Handl, J. and Knowles, J.},
biburl = {https://www.bibsonomy.org/bibtex/2ff2e22b702b8073697169910dbd91593/lee_peck},
description = {Welcome to IEEE Xplore 2.0: Improvements to the scalability of multiobjective clustering},
doi = {10.1109/CEC.2005.1554990},
interhash = {a5ad13aa88ec2dede4ef8a859a969131},
intrahash = {ff2e22b702b8073697169910dbd91593},
journal = {Evolutionary Computation, 2005. The 2005 IEEE Congress on},
keywords = {05 Handl clustering multiobjective},
month = {Sept.},
pages = { 2372-2379},
timestamp = {2008-12-03T22:16:47.000+0100},
title = {Improvements to the scalability of multiobjective clustering},
volume = 3,
year = 2005
}