Name
Abstract | ||
|---|---|---|
In this paper we present an online recursive clustering algorithm based on incremental and decremental Support Vector Machine (SVM). Developed to learn evolving clusters from non-stationary data, it is able to achieve an efficient multi-class clustering in a non-stationary environment. With a new similarity measure and different procedures (Creation, Adaptation: incremental and decremental learning, Fusion and Elimination) this classifier can provide optimal updated models of data. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-87536-9_35 | ICANN (1) |
Keywords | Field | DocType |
online recursive,decremental svm,new similarity measure,non-stationary data,non-stationary environment,different procedure,optimal updated model,online clustering,efficient multi-class,decremental learning,decremental support,vector machine,support vector machine | Fuzzy clustering,Correlation clustering,Pattern recognition,Similarity measure,Computer science,Support vector machine,Concept drift,Artificial intelligence,Cluster analysis,Classifier (linguistics),Machine learning,Recursion | Conference |
Volume | ISSN | Citations |
5163 | 0302-9743 | 2 |
PageRank | References | Authors |
0.37 | 7 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Khaled Boukharouba | 1 | 18 | 1.64 |
| Stéphane Lecoeuche | 2 | 57 | 13.03 |