Pattern preprocessing refers to the initial phase in data analysis or machine learning where raw data is transformed and organized to enhance the efficiency and accuracy of pattern recognition algorithms. This process involves cleaning, normalizing, and structuring data to ensure that subsequent analyses are both meaningful and computationally feasible.