Name
Title | ||
|---|---|---|
Single and Multiobjective Evolutionary Algorithms for Clustering Biomedical Information with Unknown Number of Clusters. |
Abstract | ||
|---|---|---|
This article presents single and multiobjective evolutionary approaches for solving the clustering problem with unknown number of clusters. Simple and ad-hoc operators are proposed, aiming to keep the evolutionary search as simple as possible in order to scale up for solving large instances. The experimental evaluation is performed considering a set of real problem instances, including a real-life problem of analyzing biomedical information in the Parkinson's disease map project. The main results demonstrate that the proposed evolutionary approaches are able to compute accurate trade-off solutions and efficiently handle the problem instance involving biomedical information. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-91641-5_9 | BIOINSPIRED OPTIMIZATION METHODS AND THEIR APPLICATIONS, BIOMA 2018 |
Keywords | DocType | Volume |
Clustering,Biomedical information,Multiobjective | Conference | 10835 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| María Eugenia Curi | 1 | 0 | 0.34 |
| Lucía Carozzi | 2 | 0 | 0.34 |
| Renzo Massobrio | 3 | 10 | 4.45 |
| Sergio Nesmachnow | 4 | 472 | 48.10 |
| Grégoire Danoy | 5 | 239 | 33.33 |
| Marek Ostaszewski | 6 | 29 | 7.04 |
| Pascal Bouvry | 7 | 493 | 56.10 |