One of the many things that need to be done because the amount of text data available on such devices is also escalating rapidly, and we need efficient ways to sort and analyze large textual datasets. One of the popular ways data mining techniques can be conducted on given data is by clustering, an effective method for grouping similar text documents so they can be processed for relevance, summarization and knowledge acquisition. This study aims to explore the use of sophisticated data mining approaches to improve clustering of textual documents, examining techniques that maximize both performance and accuracy in high-dimensional spaces of text categories. In this study we compare various clustering algorithms including but not limited to K Means, hierarchical clustering and density based algorithm with preprocessing techniques such as tokenization, stop-word removal and term frequency-inverse document frequency (TF-IDF) weighting. We also examine dimensionality reduction methods (Latent Semantic Analysis (LSA) and Principal Component Analysis (PCA)) to deal with the sparsely and complexity of textual data. The performance of the implication patterns in the clustering task on benchmark datasets is evaluated using quality metrics like silhouette score, entropy, and purity through empirical experiments. Superior clustering results show that preprocessing pipelines tailored for text, and hybrid approaches combining machine learning algorithms with text-specific enhancements, are particularly advantageous.