Name
Abstract | ||
|---|---|---|
k'-means algorithm is a new improvement of k-means algorithm. It implements a rewarding and penalizing competitive learning mechanism into the k-means paradigm such that the number of clusters can be automatically determined for a given dataset. This paper further proposes the kernelized versions of k'-means algorithms with four different discrepancy metrics. It is demonstrated by the experiments on both synthetic and real-world datasets that these kernel k'-means algorithms can automatically detect the number of actual clusters in a dataset, with a classification accuracy rate being considerably better than those of the corresponding k'-means algorithms. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-39482-9_27 | ICIC (2) |
Keywords | Field | DocType |
kernel k,real-world datasets,actual cluster,new improvement,k-means algorithm,kernelized version,clustering analysis,competitive learning mechanism,classification accuracy rate,different discrepancy metrics,corresponding k | Kernel (linear algebra),Competitive learning,Cluster (physics),k-means clustering,Pattern recognition,Radial basis function kernel,Computer science,Artificial intelligence,Kernel method,Cluster analysis,Variable kernel density estimation,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 18 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yue Zhao | 1 | 186 | 33.54 |
| Shuyi Zhang | 2 | 0 | 0.34 |
| Jinwen Ma | 3 | 841 | 74.65 |