Online clustering for crowdsourcing platform
At a glance
This project funded by the Federal Commission for Technology and
Innovation (KTI no. 12747) joins an interdisciplinary team of
researchers from the University of St. Gallen, the Zurich
University of Applied Sciences, and the University of Zurich
(Institute of Biomedical Ethics & Department of Geography) together
with a business partner Atizio (AG), an internet start-up company.
The project goal is the development of novel online text data
mining methods coupled with innovative information visualization
displays for the effective and efficient exploration of rapidly
growing online text data bases generated through crowd sourcing and
virtual idea brainstorming platforms.
The core algorithm allows for a semantic analysis in a growing text space on the basis of a self-learning neural network.
Niederberger, Thomas; Stoop, Norbert; Christen, Markus; Ott, Thomas (2012). Hebbian principal component clustering for information retrieval on a crowdsourcing platform. In: Nonlinear Dynamics of Electronic Systems, Wolfenbüttel, Germany, 11 Juli 2012 - 13 Juli 2012, 1-4.