Microarray datasets are often too large to visualise due to the high dimensionality. The self-organising map has been found useful to analyse massive complex datasets. It can be used for clustering, visualisation, and dimensionality reduction. However for visualisation purposes the SOM uses colouring schemes as a means of marking cluster boundaries on the map. The distribution of the data and the cluster structures are not faithfully portrayed. In this paper we applied the recently proposed visualisation induced Self-Organising Map (ViSOM), which directly preserves the inter-point distances of the input data on the map as well as the topology. The ViSOM algorithm regularizes the neurons so that the distances between them are proportional in both the data space and the map space. The results are similar to the Sammon mappings but with improved details on gene distributions and the flexibility to nonlinearity. The method is more suitable for larger datasets. © Springer-Verlag Berlin Heidelberg 2004.
|Title of host publication||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.|
|Number of pages||6|
|Publication status||Published - 2004|
|Event||Intelligent Data Engineering and Automated Learning (IDEAL’04) - |
Duration: 1 Jan 1824 → …
|Conference||Intelligent Data Engineering and Automated Learning (IDEAL’04)|
|Period||1/01/24 → …|