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Biclustering refers to simultaneously capturing correlations present among subsets of attributes (columns) and records (rows). It is widely used in data mining applications including biological data analysis, financial forecasting, and text mining. Biclustering algorithms are significantly more complex compared to the classical one dimensional clustering techniques, particularly those requiring multiple computing platforms for large and distributed data sets. In this paper, we develop an efficient scalable algorithm, referred to as ParRescue(Parallel Residue Co-clustering), that is capable of performing biclustering on extremely large or geographically distributed data sets. ParRescue divides the cluster tasks among processors with minimal communication costs thus making it scalable over large number of computing nodes. The proposed implementation is based on an existing sequential approach that has been modified for amenable parallel implementation. The proposed Par- Rescue algorithm has been implemented using MPI and the performance results are reported based on executions on a 64 node Linux PC cluster connected over 100 Mbits links. The experimental results show scalable performance with near linear speedups across different data and machine sizes compared to the modified sequential algorithm.