Abstract
Questions: Traditional clustering methods generally assume data are structured as discrete hyper-spheroidal clusters to be evaluated by measures of central-tendency. If vegetation data do not conform to this model, then vegetation data may be clustered incorrectly. What are the implications for cluster stability and evaluation if clusters are of irregular shape or density?
Location: Southeast Australia
Methods: We define mis-classification as the placement of a sample in a cluster other than its nearest neighbour and hypothesise that: i) optimising homogeneity incurs the cost of higher rates of mis-classification; and ii) misclassification varies with thematic scale. We comparied the performance of an algorithm (Chameleon) which operates on interconnectivity and thus is sensitive to the shape and distribution of clusters with that of three traditional algorithms over varying scales.
Results: Chameleon-derived solutions had lower rates of misclassification and only marginally higher heterogeneity than those of k-means in the range 15–60 clusters, but their metrics converged at finer thematic scales. Solutions derived by agglomerative clustering had the best metrics (and divisive clustering the worst) but both produced inferior high-level solutions clusters to those of Chameleon by merging distantly-related clusters.
Conclusions: Our results suggest that Chameleon may have an advantage over traditional algorithms at thematic scales at which data exhibit discontinuities and variable structure, potentially producing more stable solutions (due to lower rates of mis-classification), but scoring lower on traditional metrics of central-tendency. Chameleon’s advantages are less obvious in the partitioning of continuous data, however its graph-based partitioning protocol facilitates hierarchical integration of solutions.