Name
Abstract | ||
|---|---|---|
In this paper we propose is an extension of kernel k-means clustering algorithm for symbolic interval data with aggregated kernel functions. To evaluate this method, experiments with synthetic interval data set was performed and we have been compared our method with a dynamic clustering algorithm with single adaptive distance. The evaluation is based on an external cluster validity index (corrected Rand index) and the overall error rate of classification (OERC). This experiment showed the usefulness of the proposed method and the results indicate that aggregated kernel clustering algorithm gives markedly better performance on data sets considered. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICTAI.2010.133 | ICTAI), 2010 22nd IEEE International Conference |
Keywords | Field | DocType |
pattern classification,pattern clustering,aggregated kernel functions,corrected Rand index,external cluster validity index,k-means clustering,overall error classification rate,symbolic interval data | k-medians clustering,Canopy clustering algorithm,Fuzzy clustering,CURE data clustering algorithm,Correlation clustering,Pattern recognition,Artificial intelligence,Cluster analysis,Variable kernel density estimation,Machine learning,Mathematics,Single-linkage clustering | Conference |
Volume | ISSN | ISBN |
2 | 1082-3409 | 978-1-4244-8817-9 |
Citations | PageRank | References |
1 | 0.35 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anderson Costa | 1 | 1 | 0.35 |
| Bruno Pimentel | 2 | 1 | 0.35 |
| Renata Souza | 3 | 1 | 0.35 |