A Novel Approach for Semi Supervised Document Clustering with Constraint Score based Feature Supervision
S. Princiya, M. Prabakaran · IOSR Journal of Computer Engineering · 2014
Text document clustering provides an effective technique to manage a huge amount of retrieval outcome by grouping documents in a small number of meaningful classes.In unsupervised clustering method the unlabeled input data is used to estimate the parameter values.In a semi supervised document clustering both labeled and unlabeled input data is used for document clustering.A semi supervised clustering with feature supervision and constraint score is proposed in this paper.This proposed system which handles document clustering and feature Supervision simultaneously and this system finds the number of clusters automatically.Feature supervision uses pairwise constraints that performs supervision between the each documents.The semisupervised constraint score that uses both pairwise constraints and the constraint score is to compute relevant features and irrelevant feature on document data set.A variational inference algorithm uses the Dirichlet Process Mixture model for the document clustering.