Name
Title | ||
|---|---|---|
Inner matrix norms in evolving Cauchy possibilistic clustering for classification and regression from data streams. |
Abstract | ||
|---|---|---|
•A generalized novel evolving possibilistic Cauchy clustering is presented that works in an online manner on a stream of data.•As oppose to some evolving algorithms the presented approach has only few tuning parameters.•The proposed clustering is tested on different benchmark problems and compared to other algorithms.•The obtained results are promising and show that the approach can be potentially useful for a broad set of different problems. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.ins.2018.11.040 | Information Sciences |
Keywords | Field | DocType |
Data stream,Evolving clustering,Cauchy density | Data stream mining,Matrix (mathematics),Outlier,Algorithm,Cauchy distribution,Matrix norm,Artificial intelligence,Cluster analysis,Recursion,Machine learning,Mathematics,Computation | Journal |
Volume | ISSN | Citations |
478 | 0020-0255 | 2 |
PageRank | References | Authors |
0.36 | 25 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Igor Skrjanc | 1 | 354 | 52.47 |
| Saso Blazic | 2 | 151 | 29.21 |
| Edwin Lughofer | 3 | 1940 | 99.72 |
| Dejan Dovzan | 4 | 117 | 8.18 |