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Sensors 2014, 14(10), 18960-18981;

Multivariate Spatial Condition Mapping Using Subtractive Fuzzy Cluster Means

Electrical and Electronic Engineering, Auckland University of Technology, 24 St Paul Street, Auckland 1010, New Zealand
Author to whom correspondence should be addressed.
Received: 24 February 2014 / Revised: 23 September 2014 / Accepted: 24 September 2014 / Published: 13 October 2014
(This article belongs to the Section Sensor Networks)
Full-Text   |   PDF [643 KB, uploaded 13 October 2014]


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. View Full-Text
Keywords: data stream mining; sensor cloud; fuzzy clustering; wireless sensor network data stream mining; sensor cloud; fuzzy clustering; wireless sensor network
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Sabit, H.; Al-Anbuky, A. Multivariate Spatial Condition Mapping Using Subtractive Fuzzy Cluster Means. Sensors 2014, 14, 18960-18981.

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