Energy efficient data mining scheme for high dimensional data

Moh'D Alwadi, Girija CHETTY

Research output: A Conference proceeding or a Chapter in BookConference contribution

2 Citations (Scopus)
3 Downloads (Pure)

Abstract

In this paper, we propose energy efficient big data mining scheme for forest cover type and gas drift classification. Efficient machine learning and data mining techniques provide unprecedented opportunity to monitor and characterize physical environments, such as forest cover type, using low cost wireless sensor networks. The experimental validation on two different sensor network datasets, forest cover type and gas sensor array drift dataset from publicly available UCI machine learning repository. Coupled with an appropriate feature selection, the complete scheme leads towards an energy efficient protocol for intelligent monitoring of large physical environments instrumented with wireless sensor networks.
Original languageEnglish
Title of host publicationProceedings of the International Conference on Information and Communications Technologies
EditorsPhilip Samual
Place of PublicationIndia
PublisherElsevier
Pages483-490
Number of pages8
Volume46
DOIs
Publication statusPublished - 2015
EventInternational Conference on Information and Communication Technologies: ICICT 2014 - Kochi, Kochi, India
Duration: 3 Dec 20145 Dec 2014

Publication series

NameProcedia Computer Science
PublisherElsevier
Volume46
ISSN (Print)1877-0509

Conference

ConferenceInternational Conference on Information and Communication Technologies
CountryIndia
CityKochi
Period3/12/145/12/14

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Alwadi, MD., & CHETTY, G. (2015). Energy efficient data mining scheme for high dimensional data. In P. Samual (Ed.), Proceedings of the International Conference on Information and Communications Technologies (Vol. 46, pp. 483-490). (Procedia Computer Science; Vol. 46). India: Elsevier. https://doi.org/10.1016/j.procs.2015.02.047