Stream Data Mining using the MOA Framework

Massive Online Analysis (MOA) is a software framework that provides algorithms and evaluation methods for mining tasks on evolving data streams. In addition to supervised and unsupervised learning, MOA has recently been extended to support multi-label classi fication and graph mining. In this demonstrator we describe the main features of MOA and present the newly added methods for outlier detectionon streaming data. Algorithms can be compared to established baseline methods such as LOF and ABOD using standard ranking measures including Spearman rank coefficient and the AUC measure. MOA is an open source project and videos as well as tutorials are publicly available on the MOA homepage.

Authors: Kranen P., Kremer H., Jansen T., Seidl T., Bifet A., Holmes G., Pfahringer B., Read J.
Published in: The 17th International Conference on Database Systems for Advanced Applications (DASFAA), Busan, South Korea
Publisher: Springer
Language: EN
Year: 2012
Additional:

(Demo)

Pages: 309-313
ISBN: 978-3-642-29034-3
Conference: DASFAA
DOI:10.1007/978-3-642-29035-0_27
Url:DASFAA 2012
Research topic: Data Analysis and Knowledge Extraction