  NLP pipelines with limited or no labeled data, rely on unsupervised methods
for document processing. Unsupervised approaches typically depend on clustering
of terms or documents. In this paper, we introduce a novel clustering
algorithm, Vec2GC (Vector to Graph Communities), an end-to-end pipeline to
cluster terms or documents for any given text corpus. Our method uses community
detection on a weighted graph of the terms or documents, created using text
representation learning. Vec2GC clustering algorithm is a density based
approach, that supports hierarchical clustering as well.
