Stream data mining using the MOA framework

Authors:
Philipp Kranen;Hardy Kremer;Timm Jansen;Thomas Seidl;Albert Bifet;Geoff Holmes;Bernhard Pfahringer;Jesse Read
Affiliations:
Data Management and Exploration Group, RWTH Aachen University, Germany;Data Management and Exploration Group, RWTH Aachen University, Germany;Data Management and Exploration Group, RWTH Aachen University, Germany;Data Management and Exploration Group, RWTH Aachen University, Germany;Department of Computer Science, University of Waikato, Hamilton, New Zealand;Department of Computer Science, University of Waikato, Hamilton, New Zealand;Department of Computer Science, University of Waikato, Hamilton, New Zealand;Department of Computer Science, University of Waikato, Hamilton, New Zealand
Venue:
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part II
Year:
2012

Citing 5
Cited 1

MOA: Massive Online Analysis

The Journal of Machine Learning Research
Mining frequent closed graphs on evolving data streams

Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
An effective evaluation measure for clustering on evolving data streams

Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Visual evaluation of outlier detection models

DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
AnyOut: anytime outlier detection on streaming data

DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I

AnyOut: anytime outlier detection on streaming data

DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

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 classification and graph mining. In this demonstrator we describe the main features of MOA and present the newly added methods for outlier detection on 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.