In this study, we describe the use of the self-organizing map (SOM) as a metamodeling technique to design a parallel text data exploration system. Firstly, the large textual collections are divided into various small data subsets. Based on the different subsets, different unitary SOM models, i.e., base models, are then trained for word clustering map. In this phase, different SOM models are implemented in parallel to gain greater computational efficiency. Finally, a SOM-based metamodel can be produced to formulate a text category map through learning from all base models. For illustration the proposed metamodel is applied to a massive text data collection. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Lai, K. K., Yu, L., Zhou, L., & Wang, S. (2006). Self-organizing-map-based metamodeling for massive text data exploration. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3971 LNCS, pp. 1261–1266). Springer Verlag. https://doi.org/10.1007/11759966_187
Mendeley helps you to discover research relevant for your work.