A hybrid approach for generative process model with topic modelling towards efficient and dynamic document clustering

Abstract

Clustering has potential to generate and organize documents into some meaningful clusters helping in discovering latent structure in presences of unlabelled document corpora [1]. Latent Dirichlet allocation (LDA) is a widely used generative process model to deal with textual corpora in a systematic fashion. It is the model that helps in processing documents and underlying text for solving different problems [2]. Often LDA is used along with clustering techniques like K-means clustering. Another important aspect associated with processing documents is known as dimensionality reduction which improves processing performance.There are many existing methods that focused on document clustering problem. Most of the solutions,