This paper presents textual document clustering using two approaches namely cosine similarity and frequency and inverse document frequency. With the combination of these approaches a similarity measure values are generated between keywords in the documents and between the documents. Using this approach, the best related document can be identified on the basis of clustering method called correlation preserving index in which related documents are stored in an index format.