Browsing by Author "Seidl, Thomas"
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Clustering performance on evolving data streams: Assessing algorithms and evaluation measures within MOA
(IEEE Computer Society, 2010)In today's applications, evolving data streams are ubiquitous. Stream clustering algorithms were introduced to gain useful knowledge from these streams in real-time. The quality of the obtained clusterings, i.e. how good ... -
MOA: Massive Online Analysis, a framework for stream classification and clustering.
(JMLR, 2010)Massive Online Analysis (MOA) is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA is designed to deal with the challenging problem of scaling ...
