Term weighting schemes are used to identify the importance of each term with respect to a collection and assign weights to them accordingly. Document clustering uses these term weights to compare the similarity between documents. Several term weighting schemes are in use today, but none of them are specific to the clustering algorithms. The term frequency-based clustering techniques consider the documents as a bag of words while ignoring the relationship between the words. So, in this paper we focus our analysis on different term weighting schemes such as term frequency (tf), term frequencyinverse document frequency (tfidf), automatic text categorisation (ATC) without normalisation and ATC-inverse document frequency (ATCidf). In this paper, we have used the clustering tool CLUTO to experimentally study the impact of term weighting schemes on the quality of the clustering solution obtained by applying the Repeated Bisection Partitional Algorithm. Copyright © 2018 Inderscience Enterprises Ltd.