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Framework for Storing and Processing Relational Entities in Stream Mining

P. Matuszyk, and M. Spiliopoulou. Advances in Knowledge Discovery and Data Mining , volume 7819 of Lecture Notes in Computer Science, Springer Berlin Heidelberg, (2013)

Abstract

Relational stream mining involves learning a model on relational entities, which are enriched with information from further streams that reference them. To incorporate such information into the entities in an efficient incremental way, we propose a multi-threaded framework with a weighting function that prioritizes the entities delivered to the learner for learning and adaption to drift. We further propose a generator for drifting relational streams, and use it to show that our framework reaches substantial reduction of computation time.

Links and resources

DOI:
10.1007/978-3-642-37456-2_42
URL:
http://dx.doi.org/10.1007/978-3-642-37456-2_42
BibTeX key:
conf/pakdd/MatuszykS13
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