Estimating the risk of insect species invasion: Kohonen self-organising maps versus k-means clustering.

Previous work on the estimation of the invasiveness of insect pest species used a single Kohonen self-organising map (SOM) to quantify the invasion potential of each member of a set of species in relation to a particular geographic region. In this paper that method is critically compared to an alternative approach of calculating the invasive potential of insect pest species as an outcome of clustering of regional species assemblages. Data clustering was performed using SOM and k-means optimisation clustering and multiple trials were performed with each algorithm. The outcomes of these two approaches were evaluated and compared to the previously published results obtained from a single SOM. The results show firstly, due to the inherent variation between trials of the algorithms used, that multiple trials are necessary to determine reliable risk ratings, and secondly, that k-means clustering can be considered a more appropriate algorithm for this particular application, as it produces clusters of higher quality, as determined by objective cluster measures, and is far more computationally efficient than SOM.

[1]  Young-Seuk Park,et al.  Application of a self-organizing map to select representative species in multivariate analysis: A case study determining diatom distribution patterns across France , 2006, Ecol. Informatics.

[2]  Giles M. Foody,et al.  Applications of the self-organising feature map neural network in community data analysis , 1999 .

[3]  Pierre Hansen,et al.  Cluster analysis and mathematical programming , 1997, Math. Program..

[4]  Susan P. Worner,et al.  Modelling global insect pest species assemblages to determine risk of invasion , 2006 .

[5]  S. Lek,et al.  Fish assemblage patterns in the littoral zone of a European reservoir , 2007 .

[6]  Sovan Lek,et al.  A comparison of self-organizing map algorithm and some conventional statistical methods for ecological community ordination , 2001 .

[7]  Brian Everitt,et al.  Cluster analysis , 1974 .

[8]  Claude E. Shannon,et al.  The Mathematical Theory of Communication , 1950 .

[9]  Teuvo Kohonen,et al.  The self-organizing map , 1990 .

[10]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[11]  N. Gotelli Null model analysis of species co-occurrence patterns , 2000 .

[12]  John W. Sammon,et al.  A Nonlinear Mapping for Data Structure Analysis , 1969, IEEE Transactions on Computers.

[13]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[14]  P. Sneath The application of computers to taxonomy. , 1957, Journal of general microbiology.

[15]  A. Peterson,et al.  Predicting Species Invasions Using Ecological Niche Modeling: New Approaches from Bioinformatics Attack a Pressing Problem , 2001 .

[16]  Nikola Kasabov,et al.  Estimating risk of events using SOM models: A case study on invasive species establishment , 2006 .

[17]  Nikola Kasabov,et al.  Foundations Of Neural Networks, Fuzzy Systems, And Knowledge Engineering [Books in Brief] , 1996, IEEE Transactions on Neural Networks.

[18]  R. Céréghino,et al.  Spatial analysis of stream invertebrates distribution in the Adour-Garonne drainage basin (France), using Kohonen self organizing maps , 2001 .