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Vegetation may be described as the plant life of a region. The study of patterns and processes in vegetation at various scales of space and time is useful in understanding landscapes, ecological processes, environmental history and predicting ecosystem attributes such as productivity. Generalized vegetation descriptions, maps and other graphical representations of vegetation types have become fundamental to land use planning and management. They are widely used as biodiversity surrogates in conservation assessments and can provide a useful summary of many non-vegetation landscape elements such as animal habitats, agricultural suitability and the location and abundance of timber and other forest resources. Clustering vegetation data is well known machine learning problem which aims to partition the data set into subsets, so that the data in each subset share some common trait. Present study was done with an objective to study the successional changes in herbaceous vegetation in an age series of restored mined land and also analyzes them by subjecting the vegetation data to cluster analysis. The results of the study reveals that with widespread distribution and dominance of some of the prominent naturals invaders as component of both - the mined sites as well as the undisturbed natural site, the final composition of the community at the restored sites are compiled solely from the existing population of the species and the succession on restored area results in the similar community as that found on undisturbed forest in the same vicinity.
