Accurate classification of gases/odors is a classical challenge. Gas sensor arrays are generally used to enhance classification accuracy as they generate unique signature patterns for each individual gas/odor sample. However, instead of using these raw signatures directly, analyzing these signatures in certain hyperspaces also improves classification results. Most of the clusters created using existing transformations and normalization techniques are usually non-spherical and overlapping, leading to ambiguous classification results. In this paper, a new transformation called normalized difference sensor response transformation has been proposed. Here, virtual multi-sensor responses are generated from raw signature responses. These virtual responses show unambiguous association between data belonging to respective gases/odors. In addition, the signatures generated from virtual responses are grossly concentration independent, which further enhances the accuracy of classification. Experiments were conducted by using a thick-film four-element gas sensor array for four hazardous gases viz., acetone, carbon tetrachloride (CCl4), ethyl methyl ketone, and xylene. The results show cluster compaction by an average of 94.38% in terms of intra-cluster distance with respect to raw sensor responses. In addition, when compared with existing techniques, only the proposed transformation could generate non-overlapping clusters. Furthermore, minimum inter-cluster separation was 0.80 units.