Dynamic self-organising map

Dynamic self-organising map

6 We present in this paper a variation of the self-organising map algorithm where the original 7 time-dependent (learning rate and neighbourhood) learning function is replaced by a time8 invariant one. This allows for on-line and continuous learning on both static and dynamic 9 data distributions. One of the property of the newly proposed algorithm is that it does 10 not fit the magnification l...

Bregman Divergences and the Self Organising Map

We discuss Bregman divergences and the very close relationship between a class of these divergences and the regular family of exponential distributions before applying them to various topology preserving dimension reducing algorithms. We apply these methods to identification of structure in magnetic resonance images of the brain and show that different divergences reveal different structure in ...

Visualizing Random Forest with Self-Organising Map

Abstract. Random Forest (RF) is a powerful ensemble method for classification and regression tasks. It consists of decision trees set. Although, a single tree is well interpretable for human, the ensemble of trees is a black-box model. The popular technique to look inside the RF model is to visualize a RF proximity matrix obtained on data samples with Multidimensional Scaling (MDS) method. Here...

The self-organising map, robustness, self-organising criticality and power law

Growing Self Organising Map Based Exploratory Analysis of Text Data

Textual data plays an important role in the modern world. The possibilities of applying data mining techniques to uncover hidden information present in large volumes of text collections is immense. The Growing Self Organizing Map (GSOM) is a highly successful member of the Self Organising Map family and has been used as a clustering and visualisation tool across wide range of disciplines to dis...