Name
Abstract | ||
|---|---|---|
Most of the classical methods for clustering analysis require the user setting of number of clusters. To surmount this problem, in this paper a grammar-based Genetic Programming approach to automatic data clustering is presented. An innovative clustering process is conceived strictly linked to a novel cluster representation which provides intelligible information on patterns. The efficacy of the implemented partitioning system is estimated on a medical domain by exploiting expressly defined evaluation indices. Furthermore, a comparison with other clustering tools is performed. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1145/1066677.1066891 | SAC |
Keywords | Field | DocType |
grammar-based genetic programming approach,genetic programming approach,classical method,automatic data clustering,clustering analysis,novel cluster representation,intelligible information,evaluation index,medical domain,innovative clustering process,clustering tool,cluster analysis,data clustering,genetic programming | Fuzzy clustering,CURE data clustering algorithm,Correlation clustering,Computer science,Artificial intelligence,Constrained clustering,Conceptual clustering,Cluster analysis,Brown clustering,Machine learning,Single-linkage clustering | Conference |
ISBN | Citations | PageRank |
1-58113-964-0 | 11 | 0.55 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| I De Falco | 1 | 314 | 16.62 |
| Ernesto Tarantino | 2 | 361 | 42.45 |
| A. Delia Cioppa | 3 | 12 | 0.91 |
| Francesco Fontanella | 4 | 58 | 15.48 |