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Distributed hoeffding trees for pocket data mining

Stahl, F., Gaber, M. M., Bramer, M. and Yu, P. S. (2011) Distributed hoeffding trees for pocket data mining. In: International Conferance on High Performance Computing and Simulation (HPCS), 2011. IEEE, pp. 686-692. ISBN 9781612843803

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To link to this item DOI: 10.1109/HPCSim.2011.5999893

Abstract/Summary

Collaborative mining of distributed data streams in a mobile computing environment is referred to as Pocket Data Mining PDM. Hoeffding trees techniques have been experimentally and analytically validated for data stream classification. In this paper, we have proposed, developed and evaluated the adoption of distributed Hoeffding trees for classifying streaming data in PDM applications. We have identified a realistic scenario in which different users equipped with smart mobile devices run a local Hoeffding tree classifier on a subset of the attributes. Thus, we have investigated the mining of vertically partitioned datasets with possible overlap of attributes, which is the more likely case. Our experimental results have validated the efficiency of our proposed model achieving promising accuracy for real deployment.

Item Type:Book or Report Section
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:30157
Publisher:IEEE

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