  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 [Sankhy\={a} 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.
