Text mining involves analyzing large corpora of documents with thousands of words with a high level of noise content. Dimensionality reduction, noise mitigation, accurate and stable cluster formation are principal challenges of upstream analytics. This paper proposes a methodology for dimensionality as well as noise reduction using k-fold rotation estimation. Principal Component Analysis enables selecting a reduced set of dimensions (words). The resulting noise-reduced data set is the input to clustering algorithms. Experiments using benchmark data sets from the Brown corpus  and real life feedback data of a service provider show that our approach delivers improved results using the well-known performance measures recall, precision, and F-measure . We used combination of projective transforms known as principal component analysis (PCA) and visual scree plot techniques [8, 6, 12] for dimensionality reduction and a k-Fold rotation sampling technique  for noise elimination and formation of stable clusters. Experimental results with corpora of different sizes demonstrate that the approach delivers improved clustering accuracy than standard k-means clustering algorithm .