High Dimensional Text Classification using Unsupervised Machine Learning Algorithm

Document Clustering is an automatic data organization technique that greatly minimizes complexity and time. The size of high-dimensional papers poses significant concerns and needs attention, as it can lead to both positive and negative consequences, particularly in the context of document classification. Inadequate dimensional reduction may hinder the desired outcomes of document classification using reduced dimensions. This research study primarily focuses on dimensional reduction techniques, leveraging the inherent similarity property to simplify data representation while preserving essential features. This study explores various strategies aimed at classification and grouping tasks, employing diverse datasets and clustering and classification techniques. Specifically, this study proposes enhancements to the Naive Bayes classification method and K-means clustering methods. The effectiveness of the proposed approach is evaluated using standard metrics such as recall, precision, and F-score, demonstrating its potential to improve classification accuracy and efficiency.