On Bayesian “central clustering”: Application to landscape classification of Western Ghats

Abstract

Landscape classification of the well-known biodiversity hotspot, Western Ghats (mountains), on the west coast of India, is an important part of a world-wide program of monitoring biodiversity. To this end, a massive vegetation data set, consisting of 51,834 4-variate observations has been clustered into different landscapes by Nagendra and Gadgil [Current Sci. 75 (1998) 264–271]. But a study of such importance may be affected by nonuniqueness of cluster analysis and the lack of methods for quantifying uncertainty of the clusterings obtained.

Motivated by this applied problem of much scientific importance, we propose a new methodology for obtaining the global, as well as the local modes of the posterior distribution of clustering, along with the desired credible and “highest posterior density” regions in a nonparametric Bayesian framework. To meet the need of an appropriate metric for computing the distance between any two clusterings, we adopt and provide a much simpler, but accurate modification of the metric proposed in [In Felicitation Volume in Honour of Prof. B. K. Kale (2009) MacMillan]. A very fast and efficient Bayesian methodology, based on [Sankhyā Ser. B 70 (2008) 133–155], has been utilized to solve the computational problems associated with the massive data and to obtain samples from the posterior distribution of clustering on which our proposed methods of summarization are illustrated.

Clustering of the Western Ghats data using our methods yielded landscape types different from those obtained previously, and provided interesting insights concerning the differences between the results obtained by Nagendra and Gadgil [Current Sci. 75 (1998) 264–271] and us. Statistical implications of the differences are also discussed in detail, providing interesting insights into methodological concerns of the traditional clustering methods.