Enhanced Document Clustering Through Combined Latent Dirichlet Allocation and Fuzzy C-means

In the realm of document analysis, identifying and grouping documents based on their thematic content presents significant challenges, particularly when dealing with diverse and overlapping topics. Traditional methods, such as Latent Dirichlet Allocation (LDA), excel in uncovering latent themes within documents but lack inherent clustering capabilities. On the other hand, Fuzzy C-means clustering offers flexible grouping by allowing documents to belong to multiple clusters, yet it struggles with high-dimensional data and lacks semantic depth. This project addresses these challenges by integrating LDA with Fuzzy C-means to enhance the process of topic modeling and document clustering. By leveraging LDA to reduce dimensionality and provide a rich, semantic representation of document topics, and employing Fuzzy C-means to perform soft clustering on these topic distributions, this approach overcomes the limitations of each individual method. The combination enables more accurate clustering of documents based on thematic similarity while accommodating the nuanced overlap between topics. The proposed system aims to achieve improved clustering performance and interpretability, providing a more comprehensive view of document relationships and content organization. This blended approach not only refines the accuracy of document clustering but also offers greater flexibility in handling complex and overlapping topics, ultimately enhancing the effectiveness of document analysis and organization.