Unsupervised exploration of scientific articles
Alin Anton, Vladimir-Ioan Creţu · 2009
Unsupervised data exploration techniques are used for extracting relational and structural information from massifs of data. In this paper we explore a collection of ACM transactions and IEEE conferences related by the subject of high performance distributed computing - with a scientific computing flavor - in order to understand how they relate to each other. The interpreted result is proved reasonable and is defined by groups of conferences and transactions which can be scaled by their degree of abstractness and physical realization.