  Traditionally linguists have organized languages of the world as language
families modelled as trees. In this work we take a contrarian approach and
question the tree-based model that is rather restrictive. For example, the
affinity that Sanskrit independently has with languages across Indo-European
languages is better illustrated using a network model. We can say the same
about inter-relationship between languages in India, where the
inter-relationships are better discovered than assumed. To enable such a
discovery, in this paper we have made use of instance-based learning techniques
to assign language labels to words. We vocalize each word and then classify it
by making use of our custom linguistic distance metric of the word relative to
training sets containing language labels. We construct the training sets by
making use of word clusters and assigning a language and category label to that
cluster. Further, we make use of clustering coefficients as a quality metric
for our research. We believe our work has the potential to usher in a new era
in linguistics. We have limited this work for important languages in India.
This work can be further strengthened by applying Adaboost for classification
coupled with structural equivalence concepts of social network analysis.
