Improving the efficiency of a clustering genetic algorithm

Hruschka, Eduardo R., Campello, Ricardo J.G.B., and de Castro, Leandro N. (2004) Improving the efficiency of a clustering genetic algorithm. In: Lecture Notes in Computer Science (3315) pp. 861-870. From: IBERAMIA 2004: Ibero-American Conference on Artificial Intelligence, Peubla, Mexico, 22-26 November 2004.

[img] PDF (Published Version) - Published Version
Restricted to Repository staff only

View at Publisher Website:


Finding optimal clusterings is a difficult task. Most clustering methods require the number of clusters to be specified in advance, and hierarchical methods typically produce a set of clusterings. In both cases, the user has to select the number of clusters. This paper proposes improvements for a clustering genetic algorithm that is capable of finding an optimal number of clusters and their partitions automatically, based upon numeric criteria. The proposed improvements were designed to enhance the efficiency of a clustering genetic algorithm. The modified algorithms are evaluated in several simulations.

Item ID: 47659
Item Type: Conference Item (Research - E1)
ISSN: 1611-3349
Funders: FAPESP, CNPq
Date Deposited: 10 May 2017 01:26
FoR Codes: 01 MATHEMATICAL SCIENCES > 0104 Statistics > 010401 Applied Statistics @ 100%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970101 Expanding Knowledge in the Mathematical Sciences @ 100%
More Statistics

Actions (Repository Staff Only)

Item Control Page Item Control Page