Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/62973
Type: Journal article
Title: Using data clustering as a method of estimating the risk of establishment of bacterial crop diseases
Author: Watts, Michael John
Citation: Computational Ecology and Software, 2011; 1(1):1-13
Publisher: IAEES
Issue Date: 2011
ISSN: 2220-721X
School/Discipline: School of Earth and Environmental Sciences : Ecology and Evolutionary Biology
Statement of
Responsibility: 
Michael J. Watts
Abstract: Previous work has investigated the use of data clustering of regional species assemblages to estimate the relative risk of establishment of insect crop pest species. This paper describes the use of these techniques to estimate the risk posed by bacterial crop plant diseases. Two widely-used clustering algorithms, the Kohonen Self-Organising Map and the k-means clustering algorithm, were investigated. It describes how a wider variety of SOM architectures than previously used were investigated, and how both of these algorithms reacted to the addition of small amounts of random ‘noise’ to the species assemblages. The results indicate that the k-means clustering algorithm is much more computationally efficient, produces better clusters as determined by an objective measure of cluster quality and is more resistant to noise in the data than equivalent Kohonen SOM. Therefore k-means is considered to be the better algorithm for this problem.
Keywords: bacterial diseases; crop; risk; establishment; data clustering
Rights: Copyright IAEES 2011
Published version: http://www.iaees.org/publications/journals/ces/online-version.asp
Appears in Collections:Earth and Environmental Sciences publications
Environment Institute publications

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