TCSOM: Clustering Transactions Using Self-Organizing Map
Neural Processing Letters
| Hi-index | 0.00 |
The Self-Organizing Map (SOM) is a powerful tool in theexploratory phase of data mining. However, due to the dimensional conflict, the neighborhood preservation cannotalways lead to perfect topology preservation. In this paper, we establish an Expanding SOM (ESOM) to detect andpreserve better topology correspondence between the twospaces. Our experiment results demonstrate that the ESOMconstructs better mappings than the classic SOM in terms ofboth the topological and the quantization errors. Furthermore, clustering results generated by the ESOM are moreaccurate than those by the SOM.