Proteins are responsible for nearly every task of cellular life, including cell shape and inner organization, product manufacture and waste cleanup, and routine maintenance. Proteins also receive signals from outside the cell and mobilize intracellular response. Experimental procedures for protein function prediction are inherently low throughput and are thus unable to annotate a non-trivial fraction of proteins that are becoming available due to rapid advances in genome sequencing technology [15]. This has motivated the development of computational techniques that utilize a variety of high-throughput experimental data for protein function prediction. So, there is need to design algorithm to find similar functional proteomic sequence from large set of sequence database. Here we present a novel unsupervised method, called Function Finder (in short F-Func) for identification function of unannotated proteomic sequence. F-Func uses clustering of sequence information represented by numerical features, performing filtering, assigned score and meet with the criterion produces decision. Using help of producing result estimate success rate of F-Func method. Estimated success rate of F-Func methods, which is almost 70%.