An Efficient Productive Feature Selection and Document Clustering (PFS-DocC) Model for Document Clustering Document Clustering using PFS-DocC Model

In mining, document clustering pretends to diminish the document size by constructing the clustering model which is extremely essential in various web-based applications. Over the past few decades, various mining approaches are analysed and evaluated to enhance the process of document clustering to attain better results; however, in most cases, the documents are messed up and degrade the performance by reducing the level of accuracy. The data instances need to be organized and a productive summary have to be generated for all clusters. The summary or the description of the document should demonstrate the information to the users' devoid of any further analysis and helps in easier scanning of associated clusters. It is performed by identifying the relevant and most influencing features to generate the cluster. This work provides a novel approach known as Productive Feature Selection and Document Clustering (PFS-DocC) model. Initially, the productive features are selected from the input dataset DUC2004 which is a benchmark dataset. Next, the document clustering model is attempted for single and multiple clusters where the generated output has to be more extractive, generic, and clustering model. This model provides more appropriate and suitable summaries which is well-suited for web-based applications. The experimentation is carried out in online available benchmark dataset and the evaluation shows that the proposed PFS-DocC model gives superior outcomes with higher ROUGE score.