A hypergraph is a graph whose edges can connect more than two vertices (hyperedges). The clustering problem is then formulated as a finding the minimum-cut of a hypergraph. A minimum-cut is the removal of the set of hyperedges (with minimum edge weight) that separates the hypergraph into unconnected components. Again, an object maps to a vertex . Each word (feature) maps to a hyperedge connecting all vertices with non-zero frequency count of this word. The weight of this hyperedge is chosen to be the total number of occurrences in the data-set. Hence, the importance of a hyperedge during partitioning is proportional to the occurrence of the corresponding word. The minimum-cut of this hypergraph into unconnected components gives the desired clustering. We employ the HMETIS package for partitioning. An advantage of this approach is that the clustering problem can be mapped to a graph problem without the explicit computation of similarity, which makes this approach computationally efficient with assuming a (close to) linear performing hypergraph partitioner. Note that, sample-wise frequency information gets lost in this formulation since there is only a single weight associated with a hyperedge.