title: Multivariate spatial condition mapping using subtractive fuzzy cluster means.
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.
authors.affiliation: Electrical and Electronic Engineering, Auckland University of Technology, 24 St Paul Street, Auckland 1010, New Zealand. hsabit@aut.ac.nz.
authors.forename: Hakilo
authors.initials: H
authors.lastname: Sabit
authors.affiliation: Electrical and Electronic Engineering, Auckland University of Technology, 24 St Paul Street, Auckland 1010, New Zealand. aalanbuk@aut.ac.nz.
authors.forename: Adnan
authors.initials: A
authors.lastname: Al-Anbuky