This document discusses a correlation preserving indexing (CPI) based text clustering method. CPI aims to find a low dimensional semantic subspace that maximizes correlation between similar documents while minimizing correlation between dissimilar documents. It is different from other methods like LSI and LPI that use Euclidean distance. The document outlines the CPI method and evaluates it on document clustering tasks, showing it doubles the accuracy of previous correlation-based methods. Hierarchical clustering algorithms are also discussed and compared to CPI in terms of evaluation metrics.