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  •   Open Research
  • AUT Faculties
  • Faculty of Design and Creative Technologies (Te Ara Auaha)
  • School of Engineering, Computer and Mathematical Sciences - Te Kura Mātai Pūhanga, Rorohiko, Pāngarau
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Multivariate spatial condition mapping using subtractive fuzzy cluster means

Sabit, H; Al-Anbuky, A
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Multivariate spatial condition mapping using subtractive fuzzy cluster means..pdf (642.9Kb)
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http://hdl.handle.net/10292/9390
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Abstract
Wireless sensor networks are usually deployed for monitoring given physical phenomena taking place in a specific space and over a specific duration of time. The spatio-temporal distribution of these phenomena often correlates to certain physical events. To appropriately characterise these events-phenomena relationships over a given space for a given time frame, we require continuous monitoring of the conditions. WSNs are perfectly suited for these tasks, due to their inherent robustness. This paper presents a subtractive fuzzy cluster means algorithm and its application in data stream mining for wireless sensor systems over a cloud-computing-like architecture, which we call sensor cloud data stream mining. Benchmarking on standard mining algorithms, the k-means and the FCM algorithms, we have demonstrated that the subtractive fuzzy cluster means model can perform high quality distributed data stream mining tasks comparable to centralised data stream mining.
Keywords
Data stream mining; Fuzzy clustering; Sensor cloud; Wireless sensor network
Date
October 13, 2014
Source
Sensors 2014, 14(10), 18960-18981; doi:10.3390/s141018960
Item Type
Journal Article
Publisher
MDPI AG
DOI
10.3390/s141018960
Publisher's Version
http://dx.doi.org/10.3390/s141018960
Rights Statement
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).

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