@inproceedings{535b4ad789bb4ca191fc3dd59e1cd1d6,
title = "A multi-objective memetic algorithm based clustering method",
abstract = "Data clustering is a challenging problem where clustering algorithms are only based on one criterion, which is able to reveal all types of structures presenting in the data. It is unfeasible to establish a priori clustering criterion which is supposed to be more appropriate to capture the structure in the data. Clustering algorithms with different objective or criterion are essential to apply to the data in order to obtain different structures. This paper presents an attempt of using Multi-Objectives Memetic Algorithm (MOMA) for providing multi-objectives clustering. The approach is supposed to provide better clustering result with complex structured data set, as well as better ability to define optimal number of clustering with direct optimization on cluster validation.",
keywords = "Evolutionary computing, Genetic algorithm, Memetic algorithm, Multi-objectives clustering",
author = "Do, {Anh Duc} and Cho, {Siu Yeung} and Ong, {Yew Soon}",
note = "Copyright: Copyright 2012 Elsevier B.V., All rights reserved.; 2008 International Conference on Artificial Intelligence, ICAI 2008 and 2008 International Conference on Machine Learning; Models, Technologies and Applications, MLMTA 2008 ; Conference date: 14-07-2008 Through 17-07-2008",
year = "2008",
language = "English",
isbn = "1601320728",
series = "Proceedings of the 2008 International Conference on Artificial Intelligence, ICAI 2008 and Proceedings of the 2008 International Conference on Machine Learning; Models, Technologies and Applications",
pages = "881--887",
booktitle = "Proceedings of the 2008 International Conference on Artificial Intelligence, ICAI 2008 and Proceedings of the 2008 International Conference on Machine Learning; Models, Technologies and Applications",
}