Tsallis entropy was introduced as a generalization of Shannon entropy, part of nonextensive statistical mechanics. It was designed to handle systems exhibiting non-standard behaviors not adequately described by classical Boltzmann-Gibbs statistics, such as systems with long-range interactions, long memory, or fractal structures. Some applications of Tsallis entropy outside of thermodynamics include document classification, 3-dimenional surface simplification, signal analysis, and estimating optimal parameters in Gaussian mixture models. Tsallis entropy generalizes Shannon entropy by introducing a parameter that allows for tuning the measure of uncertainty to accommodate phenomena not well modeled by Shannon entropy. By utilizing Tsallis entropy, we developed a novel similarity measure, the Tsallis Entropy Correlation, in this study.