  Document clustering is an unsupervised approach in which a large collection
of documents (corpus) is subdivided into smaller, meaningful, identifiable, and
verifiable sub-groups (clusters). Meaningful representation of documents and
implicitly identifying the patterns, on which this separation is performed, is
the challenging part of document clustering. We have proposed a document
clustering technique using graph based document representation with
constraints. A graph data structure can easily capture the non-linear
relationships of nodes, document contains various feature terms that can be
non-linearly connected hence a graph can easily represents this information.
Constrains, are explicit conditions for document clustering where background
knowledge is use to set the direction for Linking or Not-Linking a set of
documents for a target clusters, thus guiding the clustering process. We deemed
clustering is an ill-define problem, there can be many clustering results.
Background knowledge can be used to drive the clustering algorithm in the right
direction. We have proposed three different types of constraints, Instance
level, corpus level and cluster level constraints. A new algorithm Constrained
HAC is also proposed which will incorporate Instance level constraints as prior
knowledge; it will guide the clustering process leading to better results.
Extensive set of experiments have been performed on both synthetic and standard
document clustering datasets, results are compared on standard clustering
measures like: purity, entropy and F-measure. Results clearly establish that
our proposed approach leads to improvement in cluster quality.
