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000276330 0247_ $$2doi$$a10.1109/MIPRO.2015.7160265
000276330 037__ $$aFZJ-2015-06790
000276330 041__ $$aEnglish
000276330 1001_ $$0P:(DE-Juel1)132239$$aRiedel, Morris$$b0$$eCorresponding author$$ufzj
000276330 1112_ $$a38th International Convention on Information and Communication Technology, Electronics and Microelectronics$$cOpatija$$d2015-05-25 - 2015-05-29$$gMIPRO 2015$$wCroatia
000276330 245__ $$aScalable and parallel machine learning algorithms for statistical data mining - Practice & experience
000276330 260__ $$bIEEE$$c2015
000276330 29510 $$a2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) : [Proceedings] - IEEE, 2015. - ISBN 978-9-5323-3082-3 -
000276330 300__ $$a204 - 209
000276330 3367_ $$0PUB:(DE-HGF)8$$2PUB:(DE-HGF)$$aContribution to a conference proceedings$$bcontrib$$mcontrib$$s1448465097_32101
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000276330 520__ $$aMany scientific datasets (e.g. earth sciences, medical sciences, etc.) increase with respect to their volume or in terms of their dimensions due to the ever increasing quality of measurement devices. This contribution will specifically focus on how these datasets can take advantage of new `big data' technologies and frameworks that often are based on parallelization methods. Lessons learned with medical and earth science data applications that require parallel clustering and classification techniques such as support vector machines (SVMs) and density-based spatial clustering of applications with noise (DBSCAN) are a substantial part of the contribution. In addition, selected experiences of related `big data' approaches and concrete mining techniques (e.g. dimensionality reduction, feature selection, and extraction methods) will be addressed too. In order to overcome identified challenges, we outline an architecture framework design that we implement with open available tools in order to enable scalable and parallel machine learning applications in distributed systems.
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000276330 7001_ $$0P:(DE-Juel1)162390$$aGoetz, M.$$b1$$ufzj
000276330 7001_ $$0P:(DE-Juel1)145217$$aRicherzhagen, M.$$b2$$ufzj
000276330 7001_ $$0P:(DE-Juel1)8832$$aGlock, P.$$b3$$ufzj
000276330 7001_ $$0P:(DE-Juel1)164357$$aBodenstein, C.$$b4$$ufzj
000276330 7001_ $$0P:(DE-Juel1)132191$$aMemon, Ahmed$$b5$$ufzj
000276330 7001_ $$0P:(DE-Juel1)132190$$aMemon, Mohammad Shahbaz$$b6$$ufzj
000276330 773__ $$a10.1109/MIPRO.2015.7160265
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000276330 9141_ $$y2015
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000276330 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)132191$$aForschungszentrum Jülich GmbH$$b5$$kFZJ
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