Abstract. The aim of our research was to apply well-known data mining techniques (such as linear neural networks, multi-layered perceptrons, probabilistic neural networks, classifi...
Marcin Paprzycki, Ajith Abraham, Ruiyuan Guo, Srin...
This paper presents an incremental and scalable learning algorithm in order to mine numeric, low dimensionality, high–cardinality, time–changing data streams. Within the Superv...
In this paper, we propose a method for discovering hidden information from large-scale item set data based on the symmetry of items. Symmetry is a fundamental concept in the theory...
One of the main problems faced by university students is deciding the right learning path based on available information such as courses, schedules and professors. In this context,...
Abstract. We propose a semantic tagger that provides high level concept information for phrases in clinical documents. It delineates such information from the statements written by...