Clustering of text documents is an important technique for enhancing automated learning. Matching is the technique used in order to relate or match the various set of related documents. Clustering groups a set of documents which are similar and dissimilar for unsupervised learning where the user has learning materials which are from raw data which requires further classification. Established feature extraction strategies intend to change over the representation of the major dimensional data set into a lower-dimensional informational collection by anticipating process through mathematical changes. The concept of feature clustering is to aggregate the features into clusters with a high level of pair wise semantic relations. Each cluster is dealt as a single new feature, and, hence, feature dimensionality can be radically lessened. HAC, K-Means, TF/IDF-weighted vectors and cosine similarities is used for the various vectors of data and is applied to text in a direct way to optimize the vectors. © 2006-2018 Asian Research Publishing Network (ARPN).