Hill-Climbing, Density-Based Clustering and Equiprobabilistic Topographic Maps
Journal of VLSI Signal Processing Systems
Scalable Clustering for Mining Local-Correlated Clusters in High Dimensions and Large Datasets
Fundamenta Informaticae - Intelligent Data Analysis in Granular Computing
| Hi-index | 0.00 |
A new unsupervised competitive learning rule is introduced, called the kernel-based maximum entropy learning rule (kMER), for equiprobabilistic topographic map formation. The application envisaged is density-based clustering. An empirical study is conducted to compare the clustering performance of kMER with that of a number of other unsupervised competitive learning rules