Name
Abstract | ||
|---|---|---|
The maximum margin clustering principle extends support vector machines to unsupervised scenarios. We present a variant of this clustering scheme that can be used in the context of interactive clustering scenarios. In particular, our approach permits the class ratios to be manually defined by the user during the fitting process. Our framework can be used at early stages of the data mining process when no or very little information is given about the true clusters and class ratios. One of the key contributions is an adapted steepest-descent-mildest-ascent optimization scheme that can be used to fine-tune maximum margin clustering solutions in an interactive manner. We demonstrate the applicability of our approach in the context of remote sensing and astronomy with training sets consisting of hundreds of thousands of patterns. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-24465-5_9 | ADVANCES IN INTELLIGENT DATA ANALYSIS XIV |
Field | DocType | Volume |
Gradient descent,Computer science,Support vector machine,Artificial intelligence,Cluster analysis,Machine learning | Conference | 9385 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
9 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fabian Gieseke | 1 | 123 | 18.21 |
| Tapio Pahikkala | 2 | 1005 | 70.68 |
| Tom Heskes | 3 | 1519 | 198.44 |